Canonical Latent Representations in Conditional Diffusion Models | Paper Citation Record

Observation aefd609a-0447-4b5f-a8f1-ccd5937c0895 · outbound

This paper cites Square at- tack: a query-efficient black-box adversarial attack via random search.

Canonical Latent Representations in Conditional Diffusion Models Square at- tack: a query-efficient black-box adversarial attack via random search

Reference 1

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Observation 125b0769-6754-4b1a-8168-1f8e6827644e · outbound

This paper cites Synthetic data from diffusion models improves imagenet classification.Transactions on Machine Learning Research.

Canonical Latent Representations in Conditional Diffusion Models Synthetic data from diffusion models improves imagenet classification.Transactions on Machine Learning Research

Reference 2

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Observation 5c834f5a-2b64-4150-8615-20374e4193fb · outbound

This paper cites Leaving reality to imagination: Robust classification via generated datasets.

Canonical Latent Representations in Conditional Diffusion Models Leaving reality to imagination: Robust classification via generated datasets

Reference 3

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Observation 6818d5e0-542f-40e3-b0f8-388c47374c3b · outbound

This paper cites All are worth words: A vit backbone for diffusion models.

Canonical Latent Representations in Conditional Diffusion Models All are worth words: A vit backbone for diffusion models

Reference 4

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Observation c7a9cab1-8665-49ec-b550-57306d4d4dc0 · outbound

This paper cites Label-efficient semantic segmentation with diffusion models.

Canonical Latent Representations in Conditional Diffusion Models Label-efficient semantic segmentation with diffusion models

Reference 5

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Observation ee948ce8-15b9-4e04-bbec-1567b84ed3e7 · outbound

This paper cites Are we done with ImageNet?.

Canonical Latent Representations in Conditional Diffusion Models Are we done with ImageNet?

Reference 6

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Observation 0ee4a14b-7e53-4dad-bf55-d5c97f32d1fd · outbound

This paper cites Towards evaluating the robustness of neural networks.

Canonical Latent Representations in Conditional Diffusion Models Towards evaluating the robustness of neural networks

Reference 7

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Observation ced26cfb-ce34-4ffb-9d28-94eb8d71535b · outbound

This paper cites Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–10, 2023.

Canonical Latent Representations in Conditional Diffusion Models Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–10, 2023

Reference 8

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Observation 3227b04b-c266-45b6-bfe4-f0e834184a3c · outbound

This paper cites Exploring low- dimensional subspace in diffusion models for controllable image editing.

Canonical Latent Representations in Conditional Diffusion Models Exploring low- dimensional subspace in diffusion models for controllable image editing

Reference 9

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Observation 9effd71f-db1c-45e4-beac-2033421c43bb · outbound

This paper cites Deconstructing Denoising Diffusion Models for Self-Supervised Learning.

Canonical Latent Representations in Conditional Diffusion Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 10

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Observation 7eeae5a2-6290-4f60-9a37-63f43aae968f · outbound

This paper cites Multilinear operator networks.

Canonical Latent Representations in Conditional Diffusion Models Multilinear operator networks

Reference 11

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Observation 3988629e-b9b8-4031-bae2-648ff882d913 · outbound

This paper cites Deep feature factorization for concept discovery.

Canonical Latent Representations in Conditional Diffusion Models Deep feature factorization for concept discovery

Reference 12

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Observation f3682554-41db-427b-a189-bb4ed0ca17bb · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Canonical Latent Representations in Conditional Diffusion Models Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 13

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Observation 67851f18-eec0-49af-8cd3-1ca94cc422ad · outbound

This paper cites Aligning model and macaque inferior temporal cortex representations improves model-to-human behavioral alignment and adversarial robust- ness.

Canonical Latent Representations in Conditional Diffusion Models Aligning model and macaque inferior temporal cortex representations improves model-to-human behavioral alignment and adversarial robust- ness

Reference 14

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Observation 50b7ac84-1072-4c1a-b00d-a4da6c160023 · outbound

This paper cites ImageNet: A large- scale hierarchical image database.

Canonical Latent Representations in Conditional Diffusion Models ImageNet: A large- scale hierarchical image database

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.870193Z

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Observation 9c2bc4f0-a5a8-4269-ba49-0c084238c3d4 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Canonical Latent Representations in Conditional Diffusion Models Diffusion models beat gans on image synthesis

Reference 16

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Observation ea94386f-91f0-4430-bba6-5ca4484b711d · outbound

This paper cites On robustness and transferability of convolutional neural networks.

Canonical Latent Representations in Conditional Diffusion Models On robustness and transferability of convolutional neural networks

Reference 17

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Observation 4e0e7709-6b21-4752-b877-18b2e3aacf15 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Canonical Latent Representations in Conditional Diffusion Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.881296Z

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Observation 845f5a72-458e-4302-b44b-115c09fe19c6 · outbound

This paper cites DyTox: Trans- formers for continual learning with dynamic token expansion.

Canonical Latent Representations in Conditional Diffusion Models DyTox: Trans- formers for continual learning with dynamic token expansion

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.885006Z

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Observation ed2a13f1-21cb-4cd4-986c-1a86d8f8034c · outbound

This paper cites DreamDA: Generative Data Augmentation with Diffusion Models.

Canonical Latent Representations in Conditional Diffusion Models DreamDA: Generative Data Augmentation with Diffusion Models

Reference 20

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Observation 9766b5ca-425d-4b0a-ae21-8db12d912a18 · outbound

This paper cites Improving robustness using generated data.Advances in Neural Information Processing Systems, 34:4218–4233, 2021.

Canonical Latent Representations in Conditional Diffusion Models Improving robustness using generated data.Advances in Neural Information Processing Systems, 34:4218–4233, 2021

Reference 21

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Observation 95d8f437-023f-41c2-be24-a02637fff5ee · outbound

This paper cites Discovering interpretable directions in the semantic latent space of diffusion models.

Canonical Latent Representations in Conditional Diffusion Models Discovering interpretable directions in the semantic latent space of diffusion models

Reference 22

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Observation aa9bc5f7-c364-4f55-9cbd-c4484930433e · outbound

This paper cites Deep residual learning for im- age recognition.

Canonical Latent Representations in Conditional Diffusion Models Deep residual learning for im- age recognition

Reference 23

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Observation 50462cf0-57bc-436b-a1ce-ab40a0b262cb · outbound

This paper cites Is synthetic data from generative models ready for image recognition? InThe Eleventh International Conference on Learning Representations, 2023.

Canonical Latent Representations in Conditional Diffusion Models Is synthetic data from generative models ready for image recognition? InThe Eleventh International Conference on Learning Representations, 2023

Reference 24

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Observation 68a2cbbc-45c8-4ae7-ac0e-31077011f53a · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

Canonical Latent Representations in Conditional Diffusion Models Benchmarking neural network robustness to common corruptions and perturbations

Reference 25

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Observation 35f46db6-8b4d-4567-a125-d411ba5b9966 · outbound

This paper cites Prompt-to-prompt image editing with cross-attention control.

Canonical Latent Representations in Conditional Diffusion Models Prompt-to-prompt image editing with cross-attention control

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:07.065431Z

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Observation cb087050-c55d-4c84-9f50-d6222e925601 · outbound

This paper cites Classifier-free diffusion guidance.

Canonical Latent Representations in Conditional Diffusion Models Classifier-free diffusion guidance

Reference 27

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Observation c2d60b5d-8e0a-4784-a029-9a66a023f170 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020.

Canonical Latent Representations in Conditional Diffusion Models Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020

Reference 28

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Observation d86e746e-95b9-4014-afc4-1747506fddf1 · outbound

This paper cites Dif- fusemix: Label-preserving data augmentation with diffusion models.

Canonical Latent Representations in Conditional Diffusion Models Dif- fusemix: Label-preserving data augmentation with diffusion models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.928077Z

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Observation 015b3723-2de8-4ca2-9c60-3ea8af17d628 · outbound

This paper cites Training-free content injection using h-space in diffusion models.

Canonical Latent Representations in Conditional Diffusion Models Training-free content injection using h-space in diffusion models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:07.034271Z

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Observation 35d1ad5b-15a5-4620-ac9b-5beaf49d22cc · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in Neural Information Processing Systems, 35: 26565–26577, 2022.

Canonical Latent Representations in Conditional Diffusion Models Elucidating the design space of diffusion-based generative models.Advances in Neural Information Processing Systems, 35: 26565–26577, 2022

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.934027Z

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Observation a173b1f5-d8ff-4ed8-b72d-a57b0c70c7a9 · outbound

This paper cites Guiding a diffusion model with a bad version of itself.Advances in Neural Information Processing Systems, 37:52996–53021, 2024.

Canonical Latent Representations in Conditional Diffusion Models Guiding a diffusion model with a bad version of itself.Advances in Neural Information Processing Systems, 37:52996–53021, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:07.016507Z

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Observation 9fa14663-c696-45b6-9996-d607bbd26b87 · outbound

This paper cites Analyzing and improving the training dynamics of diffusion models.

Canonical Latent Representations in Conditional Diffusion Models Analyzing and improving the training dynamics of diffusion models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.941440Z

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Observation 85ee9015-f40e-41c4-b7f1-78ab93da146a · outbound

This paper cites Supervised contrastive learning.Advances in Neural Information Processing Systems, 33:18661–18673, 2020.

Canonical Latent Representations in Conditional Diffusion Models Supervised contrastive learning.Advances in Neural Information Processing Systems, 33:18661–18673, 2020

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.998569Z

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Observation 65cd1191-ea96-446b-92b3-91d6b426a845 · outbound

This paper cites Torchattacks: A PyTorch Repository for Adversarial Attacks.

Canonical Latent Representations in Conditional Diffusion Models Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.948566Z

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Observation 0c34635f-b857-40cf-8b78-c545b826e079 · outbound

This paper cites Dense text-to-image generation with attention modulation.

Canonical Latent Representations in Conditional Diffusion Models Dense text-to-image generation with attention modulation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.952392Z

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Observation c4dc148c-58e6-4893-b221-aa544c153346 · outbound

This paper cites Adam: A method for stochastic optimization.

Canonical Latent Representations in Conditional Diffusion Models Adam: A method for stochastic optimization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.980719Z

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Observation e2a95cbf-f45b-410e-8a54-05219704a9ef · outbound

This paper cites Auto-Encoding Variational Bayes.

Canonical Latent Representations in Conditional Diffusion Models Auto-Encoding Variational Bayes

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.958794Z

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Observation 80e078e0-7efb-4062-829a-d8d6eb340fad · outbound

This paper cites Similarity of neural network representations revisited.

Canonical Latent Representations in Conditional Diffusion Models Similarity of neural network representations revisited

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.962148Z

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Observation 10f56f37-5f5b-424a-8699-e965766c99fa · outbound

This paper cites Learning multiple layers of features from tiny images.

Canonical Latent Representations in Conditional Diffusion Models Learning multiple layers of features from tiny images

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.965632Z

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Observation 564c4a3e-2a90-4253-9b70-6578bcebcec6 · outbound

This paper cites Diffusion models already have a semantic latent space.

Canonical Latent Representations in Conditional Diffusion Models Diffusion models already have a semantic latent space

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.957996Z

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Observation b90a2e36-b5c4-40c0-b805-3b2fdb404417 · outbound

This paper cites Applying guidance in a limited interval improves sample and distribution quality in diffusion models.

Canonical Latent Representations in Conditional Diffusion Models Applying guidance in a limited interval improves sample and distribution quality in diffusion models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.948215Z

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Observation 357bf731-9233-46d7-a67e-7966ede97e23 · outbound

This paper cites Your diffusion model is secretly a zero-shot classifier.

Canonical Latent Representations in Conditional Diffusion Models Your diffusion model is secretly a zero-shot classifier

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.938551Z

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Observation 3b108cd8-0bd3-46b3-9564-b4879e51c54e · outbound

This paper cites Dreamteacher: Pretraining image backbones with deep generative models.

Canonical Latent Representations in Conditional Diffusion Models Dreamteacher: Pretraining image backbones with deep generative models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.928203Z

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Observation 293b5864-64b5-4efe-8f1e-5f59f7766fc6 · outbound

This paper cites Towards understanding cross and self-attention in stable diffusion for text-guided image editing.

Canonical Latent Representations in Conditional Diffusion Models Towards understanding cross and self-attention in stable diffusion for text-guided image editing

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.916711Z

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Observation eda7b157-c55e-4d45-88c0-2666c1afea4e · outbound

This paper cites Swin Transformer: Hierarchical vision transformer using shifted windows.

Canonical Latent Representations in Conditional Diffusion Models Swin Transformer: Hierarchical vision transformer using shifted windows

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.906704Z

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Observation b993c358-5730-46f5-b41d-08dcd5beac3a · outbound

This paper cites Swin Transformer v2: Scaling up capacity and resolution.

Canonical Latent Representations in Conditional Diffusion Models Swin Transformer v2: Scaling up capacity and resolution

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.896917Z

Source-reported events for the cited work

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Observation a0c82347-d2bd-4006-a24a-5b2cbe263710 · outbound

This paper cites Challenging common assumptions in the unsupervised learning of disentangled representations.

Canonical Latent Representations in Conditional Diffusion Models Challenging common assumptions in the unsupervised learning of disentangled representations

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.885411Z

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Observation 68b0ddef-2427-4cb6-adde-5c258528b479 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Canonical Latent Representations in Conditional Diffusion Models Towards deep learning models resistant to adversarial attacks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.874823Z

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Observation 5a05246a-b6a7-4904-a399-ad702efc8e0a · outbound

This paper cites Umap: Uniform manifold approximation and projection.Journal of Open Source Software, 3(29):861, 2018.

Canonical Latent Representations in Conditional Diffusion Models Umap: Uniform manifold approximation and projection.Journal of Open Source Software, 3(29):861, 2018

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.000323Z

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Observation 5f962cac-24a3-443e-9c16-c30482f3a4d0 · outbound

This paper cites Not all diffusion model activations have been evaluated as discriminative features.Advances in Neural Information Processing Systems, 37:55141–55177, 2024.

Canonical Latent Representations in Conditional Diffusion Models Not all diffusion model activations have been evaluated as discriminative features.Advances in Neural Information Processing Systems, 37:55141–55177, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.858806Z

Source-reported events for the cited work

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Observation 63cb6019-dfd1-4ee5-8645-4bfc14712b81 · outbound

This paper cites Diffusion Models Beat GANs on Image Classification.

Canonical Latent Representations in Conditional Diffusion Models Diffusion Models Beat GANs on Image Classification

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.007017Z

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Observation 98ea5bb0-9187-4590-bb4f-49cf01d3db09 · outbound

This paper cites Do text-free diffusion models learn discriminative visual representations? InEuropean Conference on Computer Vision, pages 253–272.

Canonical Latent Representations in Conditional Diffusion Models Do text-free diffusion models learn discriminative visual representations? InEuropean Conference on Computer Vision, pages 253–272

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.848806Z

Source-reported events for the cited work

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Observation fcdd84c4-8723-4a85-bf4c-e3741604fd96 · outbound

This paper cites Relational knowledge distillation.

Canonical Latent Representations in Conditional Diffusion Models Relational knowledge distillation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.013359Z

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Observation 1cb3a928-2570-4009-8b34-1d0a63ace497 · outbound

This paper cites Understanding the latent space of diffusion models through the lens of riemannian geometry.Advances in Neural Information Processing Systems, 36:24129–24142, 2023.

Canonical Latent Representations in Conditional Diffusion Models Understanding the latent space of diffusion models through the lens of riemannian geometry.Advances in Neural Information Processing Systems, 36:24129–24142, 2023

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.016712Z

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Observation cf113a27-49e0-4fd8-96f1-4a3a6d3aeb99 · outbound

This paper cites Scalable diffusion models with transformers.

Canonical Latent Representations in Conditional Diffusion Models Scalable diffusion models with transformers

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.019983Z

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Observation 461e583d-a8de-4d13-914c-d756354db222 · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis.

Canonical Latent Representations in Conditional Diffusion Models Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.023217Z

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Observation ff0dd2d6-f606-4d04-902f-b34054f761c5 · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

Canonical Latent Representations in Conditional Diffusion Models High- resolution image synthesis with latent diffusion models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.027076Z

Source-reported events for the cited work

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Observation 7ad93a6e-8584-4eba-8ae8-b1c71746ccbb · outbound

This paper cites Fitnets: Hints for thin deep nets.

Canonical Latent Representations in Conditional Diffusion Models Fitnets: Hints for thin deep nets

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.803239Z

Source-reported events for the cited work

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Observation 509087ab-7875-4e7a-b8de-6b56d1097441 · outbound

This paper cites Distilling representational similarity using centered kernel alignment (cka).

Canonical Latent Representations in Conditional Diffusion Models Distilling representational similarity using centered kernel alignment (cka)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.792353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c743abac-aeb0-4911-84eb-fb442a79a0b2 · outbound

This paper cites Fake it till you make it: Learning transferable representations from synthetic imagenet clones.

Canonical Latent Representations in Conditional Diffusion Models Fake it till you make it: Learning transferable representations from synthetic imagenet clones

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.779855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be0591a2-ca1c-4aa7-9707-2c1b867a86ad · outbound

This paper cites Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in Neural Information Processing Systems, 35:25278–25294, 2022.

Canonical Latent Representations in Conditional Diffusion Models Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in Neural Information Processing Systems, 35:25278–25294, 2022

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.041282Z

Source-reported events for the cited work

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Observation 61008af7-2e96-4721-9e76-a994a9009922 · outbound

This paper cites Robust learning meets generative models: Can proxy distributions improve adversarial robustness? InInternational Conference on Learning Representations, 2022.

Canonical Latent Representations in Conditional Diffusion Models Robust learning meets generative models: Can proxy distributions improve adversarial robustness? InInternational Conference on Learning Representations, 2022

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.762517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 573046e9-ca67-4e24-b713-6ea9a55baa84 · outbound

This paper cites Diffaug: A diffuse-and- denoise augmentation for training robust classifiers.Advances in Neural Information Processing Systems, 37:20745–20785, 2024.

Canonical Latent Representations in Conditional Diffusion Models Diffaug: A diffuse-and- denoise augmentation for training robust classifiers.Advances in Neural Information Processing Systems, 37:20745–20785, 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.751842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f1e748f6-a1ea-4f27-9218-e326e1950215 · outbound

This paper cites Denoising diffusion implicit models.

Canonical Latent Representations in Conditional Diffusion Models Denoising diffusion implicit models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.741966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ecf59d83-9cd2-4e0e-9acb-f8967132173e · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Canonical Latent Representations in Conditional Diffusion Models Score-based generative modeling through stochastic differential equations

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.054501Z

Source-reported events for the cited work

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Observation 116b09f5-c4b8-47fc-ad1b-5921b876b4f7 · outbound

This paper cites Latent traversals in generative models as potential flows.

Canonical Latent Representations in Conditional Diffusion Models Latent traversals in generative models as potential flows

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.725280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3cc45e03-6942-4e46-9b7f-f6e81b29484f · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Canonical Latent Representations in Conditional Diffusion Models Training data-efficient image transformers & distillation through attention

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.060699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50513ada-42b4-4778-b9c8-fc869df6cdac · outbound

This paper cites Deit iii: Revenge of the vit.

Canonical Latent Representations in Conditional Diffusion Models Deit iii: Revenge of the vit

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.708676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d1677e40-8dd5-4a10-9a35-1ebf5d12cfe8 · outbound

This paper cites Effective data augmentation with diffusion models.

Canonical Latent Representations in Conditional Diffusion Models Effective data augmentation with diffusion models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.698602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dde83dc1-4f14-49dd-988b-f7edd7a9649e · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017.

Canonical Latent Representations in Conditional Diffusion Models Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.070204Z

Source-reported events for the cited work

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Observation 11b91c10-a927-46b9-a9d1-2dff5351f0dd · outbound

This paper cites Diffusion model learns low-dimensional distributions via subspace clustering.

Canonical Latent Representations in Conditional Diffusion Models Diffusion model learns low-dimensional distributions via subspace clustering

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.681975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 83b175ba-be99-4681-9159-8ac41b8c34c2 · outbound

This paper cites Better diffusion models further improve adversarial training.

Canonical Latent Representations in Conditional Diffusion Models Better diffusion models further improve adversarial training

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.672287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1155a7ea-d4db-42e7-b9d8-49ed73df54a9 · outbound

This paper cites Pytorch image models.

Canonical Latent Representations in Conditional Diffusion Models Pytorch image models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.078877Z

Source-reported events for the cited work

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Observation b8e57d0a-cc54-4819-ad29-f0fc818f0195 · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforce- ment learning.Machine Learning, 8:229–256, 1992.

Canonical Latent Representations in Conditional Diffusion Models Simple statistical gradient-following algorithms for connectionist reinforce- ment learning.Machine Learning, 8:229–256, 1992

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.655684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 89cb3aa6-dd05-4962-af96-dc7d32f00c69 · outbound

This paper cites Convnext v2: Co-designing and scaling convnets with masked autoencoders.

Canonical Latent Representations in Conditional Diffusion Models Convnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.085389Z

Source-reported events for the cited work

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Observation cfaca2e4-a451-4ca1-b6a2-bfbb0bf247d7 · outbound

This paper cites Verbs semantics and lexical selection.

Canonical Latent Representations in Conditional Diffusion Models Verbs semantics and lexical selection

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.638051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5656eb0c-045b-4206-80f1-9ddaec18c9f4 · outbound

This paper cites Denoising diffusion autoencoders are unified self-supervised learners.

Canonical Latent Representations in Conditional Diffusion Models Denoising diffusion autoencoders are unified self-supervised learners

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.091805Z

Source-reported events for the cited work

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Observation 9c2f70e4-428d-4073-b601-25ee94fa72f2 · outbound

This paper cites Noise or signal: The role of image backgrounds in object recognition.

Canonical Latent Representations in Conditional Diffusion Models Noise or signal: The role of image backgrounds in object recognition

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.620538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 45fefd4b-d541-4f05-a7bc-9b599a106462 · outbound

This paper cites Open-vocabulary panoptic segmentation with text-to-image diffusion models.

Canonical Latent Representations in Conditional Diffusion Models Open-vocabulary panoptic segmentation with text-to-image diffusion models

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.610442Z

Source-reported events for the cited work

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Observation 73320a96-3d8a-417f-b338-a0670c782b59 · outbound

This paper cites Adanca: Neural cellular automata as adaptors for more robust vision transformer.

Canonical Latent Representations in Conditional Diffusion Models Adanca: Neural cellular automata as adaptors for more robust vision transformer

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.599782Z

Source-reported events for the cited work

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Observation b9296880-a071-4eeb-98c2-1c118c331da8 · outbound

This paper cites DER: Dynamically expandable representation for class incremental learning.

Canonical Latent Representations in Conditional Diffusion Models DER: Dynamically expandable representation for class incremental learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.589288Z

Source-reported events for the cited work

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Observation 642bafc0-6cd8-42d7-9430-b78643169238 · outbound

This paper cites Diffusion model as representation learner.

Canonical Latent Representations in Conditional Diffusion Models Diffusion model as representation learner

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.238194Z

Source-reported events for the cited work

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Observation b58482f9-96c8-449f-940f-8dd15e0dbd04 · outbound

This paper cites Vitkd: Feature-based knowledge distillation for vision transformers.

Canonical Latent Representations in Conditional Diffusion Models Vitkd: Feature-based knowledge distillation for vision transformers

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.572938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b444d1ab-c7dd-47de-9e8d-df896be911ef · outbound

This paper cites Distill vision transformers to cnns via low-rank representation approximation.

Canonical Latent Representations in Conditional Diffusion Models Distill vision transformers to cnns via low-rank representation approximation

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.563196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 920357cc-84a3-4eef-9ece-143c176b2acd · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models.Transactions on Machine Learning Research.

Canonical Latent Representations in Conditional Diffusion Models Coca: Contrastive captioners are image-text foundation models.Transactions on Machine Learning Research

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.552850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8c1a68d9-b9c3-4367-a973-69145458d5aa · outbound

This paper cites S2-mlp: Spatial-shift mlp architecture for vision.

Canonical Latent Representations in Conditional Diffusion Models S2-mlp: Spatial-shift mlp architecture for vision

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.542933Z

Source-reported events for the cited work

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Observation a53c44a6-4147-499f-89ce-2facf2cc946a · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Canonical Latent Representations in Conditional Diffusion Models Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.253704Z

Source-reported events for the cited work

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Observation b77088d2-89cb-45f7-b648-592ec4e476dd · outbound

This paper cites Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer.

Canonical Latent Representations in Conditional Diffusion Models Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.256762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5492d291-b192-4cd5-9f92-5eac2bd45be2 · outbound

This paper cites Mixup: Beyond empirical risk minimization.

Canonical Latent Representations in Conditional Diffusion Models Mixup: Beyond empirical risk minimization

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.517850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c04054e-b62e-4ec5-84ed-d0346a0f9262 · outbound

This paper cites Three things we need to know about transferring stable diffusion to visual dense prediction tasks.

Canonical Latent Representations in Conditional Diffusion Models Three things we need to know about transferring stable diffusion to visual dense prediction tasks

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.508116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be462ed3-ab6a-49ec-bade-ebbf90d2bb1a · outbound

This paper cites Scalable deep k-subspace clustering.

Canonical Latent Representations in Conditional Diffusion Models Scalable deep k-subspace clustering

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.498393Z

Source-reported events for the cited work

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Observation 7310e204-a4fe-43b3-911e-6409f00779af · outbound

This paper cites Unsupervised representation learning from pre- trained diffusion probabilistic models.Advances in Neural Information Processing Systems, 35: 22117–22130, 2022.

Canonical Latent Representations in Conditional Diffusion Models Unsupervised representation learning from pre- trained diffusion probabilistic models.Advances in Neural Information Processing Systems, 35: 22117–22130, 2022

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.488675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d102d14a-41d6-4280-9733-1bc5aa52339d · outbound

This paper cites Unleashing text- to-image diffusion models for visual perception.

Canonical Latent Representations in Conditional Diffusion Models Unleashing text- to-image diffusion models for visual perception

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.478175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 24aa2d01-7d7b-4ffb-a1ac-24c87581810f · outbound

This paper cites Understanding the robustness in vision transformers.

Canonical Latent Representations in Conditional Diffusion Models Understanding the robustness in vision transformers

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.467822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fac85dd3-593e-4578-8a9f-0b99571ba276 · outbound

This paper cites Golden Noise for Diffusion Models: A Learning Framework.

Canonical Latent Representations in Conditional Diffusion Models Golden Noise for Diffusion Models: A Learning Framework

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:06.280415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 866980af-5200-462a-9911-ad13787bcfb0 · outbound

This paper cites Rethinking centered kernel alignment in knowledge distillation.

Canonical Latent Representations in Conditional Diffusion Models Rethinking centered kernel alignment in knowledge distillation

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.457244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 87e83335-3150-4c53-841d-a967306dacab · outbound

This paper cites converging.

Canonical Latent Representations in Conditional Diffusion Models converging

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.445184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 33ce880c-9268-438f-813b-4fe8bae06788 · outbound

This paper cites The input size is 224.

Canonical Latent Representations in Conditional Diffusion Models The input size is 224

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.435875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9cfbeec4-22dd-420e-b3aa-a175d89a9edb · outbound

This paper cites The input size is 256.

Canonical Latent Representations in Conditional Diffusion Models The input size is 256

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:43:06.425311Z

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