Automated Learning of Semantic Embedding Representations for Diffusion Models | Paper Citation Record

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This paper cites Denoising diffusion probabilistic models.

Automated Learning of Semantic Embedding Representations for Diffusion Models Denoising diffusion probabilistic models

Reference 1

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

Automated Learning of Semantic Embedding Representations for Diffusion Models Score-based generative modeling through stochastic differential equations

Reference 2

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Observation 29b9de77-a0ef-46c0-92c6-bbba7637b0fd · outbound

This paper cites Denoising diffusion implicit models.

Automated Learning of Semantic Embedding Representations for Diffusion Models Denoising diffusion implicit models

Reference 3

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This paper cites Imagic: Text-based real image editing with diffusion models.

Automated Learning of Semantic Embedding Representations for Diffusion Models Imagic: Text-based real image editing with diffusion models

Reference 4

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Observation 8748ed22-e6b7-4b6e-9f7b-3aac319eea16 · outbound

This paper cites Palette: Image-to-image diffu- sion models.

Automated Learning of Semantic Embedding Representations for Diffusion Models Palette: Image-to-image diffu- sion models

Reference 5

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Observation fbf92f15-d407-48f3-9f0a-b0532822dfcb · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic models.

Automated Learning of Semantic Embedding Representations for Diffusion Models Repaint: Inpainting using denoising diffusion probabilistic models

Reference 6

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Observation 71880693-cd14-4219-86d4-700e14a8ba3b · outbound

This paper cites Inversion-based style transfer with diffusion models.

Automated Learning of Semantic Embedding Representations for Diffusion Models Inversion-based style transfer with diffusion models

Reference 7

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Observation e98bb815-2f9c-4ee5-807e-def2f484f910 · outbound

This paper cites Stylediffusion: Controllable disentangled style transfer via diffusion models.

Automated Learning of Semantic Embedding Representations for Diffusion Models Stylediffusion: Controllable disentangled style transfer via diffusion models

Reference 8

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Observation adbaa283-e949-4925-b982-f9f02895fff5 · outbound

This paper cites Pix2video: Video editing using image diffusion.

Automated Learning of Semantic Embedding Representations for Diffusion Models Pix2video: Video editing using image diffusion

Reference 9

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Observation d961f33c-3d2a-4a38-82e0-b5d8fa480263 · outbound

This paper cites Diffusion video autoencoders: Toward temporally consistent face video editing via disentangled video encoding.

Automated Learning of Semantic Embedding Representations for Diffusion Models Diffusion video autoencoders: Toward temporally consistent face video editing via disentangled video encoding

Reference 10

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Observation 2d6d5bbc-1cd3-4185-ad4d-8b5e75bd189e · outbound

This paper cites To recognize shapes, first learn to generate images.

Automated Learning of Semantic Embedding Representations for Diffusion Models To recognize shapes, first learn to generate images

Reference 11

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Observation 9d5d9948-e4da-48a2-8514-178cd562ef1d · outbound

This paper cites Spectraldiff: A generative framework for hyperspectral image classification with diffusion models.

Automated Learning of Semantic Embedding Representations for Diffusion Models Spectraldiff: A generative framework for hyperspectral image classification with diffusion models

Reference 12

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Observation 9676eb58-230c-49ae-8895-76f65678d158 · outbound

This paper cites Diffusion models as masked autoencoders.

Automated Learning of Semantic Embedding Representations for Diffusion Models Diffusion models as masked autoencoders

Reference 13

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Observation 96a41299-b14a-48e3-9c69-275957612fc1 · outbound

This paper cites Rfir: A lightweight network for retinal fundus image restoration.

Automated Learning of Semantic Embedding Representations for Diffusion Models Rfir: A lightweight network for retinal fundus image restoration

Reference 14

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Observation f240d4b8-4ac9-423d-853d-ceb74d9ac7fb · outbound

This paper cites Learning affinity from attention: End-to-end weakly- supervised semantic segmentation with transformers.

Automated Learning of Semantic Embedding Representations for Diffusion Models Learning affinity from attention: End-to-end weakly- supervised semantic segmentation with transformers

Reference 15

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Observation 52f263c6-31d7-44a2-84cf-58dece7a7a37 · outbound

This paper cites A multi-focus image fusion method based on attention mechanism and supervised learning.

Automated Learning of Semantic Embedding Representations for Diffusion Models A multi-focus image fusion method based on attention mechanism and supervised learning

Reference 16

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Observation de63d4e9-3ce4-4ea9-a7f0-2a5a4cc1258e · outbound

This paper cites Masked Diffusion as Self-supervised Representation Learner.

Automated Learning of Semantic Embedding Representations for Diffusion Models Masked Diffusion as Self-supervised Representation Learner

Reference 17

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Observation 197bfa3b-2441-4832-9d04-926036cb4765 · outbound

This paper cites Diffusion model as representation learner.

Automated Learning of Semantic Embedding Representations for Diffusion Models Diffusion model as representation learner

Reference 18

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Observation 6ca1f529-ba0d-4f47-b3a0-3c5f4a19c433 · outbound

This paper cites Text-to-image diffusion models are zero shot classifiers.

Automated Learning of Semantic Embedding Representations for Diffusion Models Text-to-image diffusion models are zero shot classifiers

Reference 19

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Observation be9d65fa-f7a1-4b56-923a-214c505f2175 · outbound

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

Automated Learning of Semantic Embedding Representations for Diffusion Models Your diffusion model is secretly a zero-shot classifier

Reference 20

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This paper cites Dif- fusion models already have a semantic latent space.

Automated Learning of Semantic Embedding Representations for Diffusion Models Dif- fusion models already have a semantic latent space

Reference 21

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Observation fcecddff-5f85-4900-a5eb-7b84b16608a9 · outbound

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

Automated Learning of Semantic Embedding Representations for Diffusion Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 22

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Observation 7aec1e1b-b5d7-4e82-8396-dff6626096ec · outbound

This paper cites Extracting and composing robust features with denoising autoencoders.

Automated Learning of Semantic Embedding Representations for Diffusion Models Extracting and composing robust features with denoising autoencoders

Reference 23

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Observation 6b2d52ce-bce6-4587-b4c2-85ab86e2b526 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Automated Learning of Semantic Embedding Representations for Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 24

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Observation 40876218-5f4d-48ae-8315-d3c00b021ecd · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Automated Learning of Semantic Embedding Representations for Diffusion Models Photorealistic text-to-image diffusion models with deep language understanding

Reference 25

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Observation 130528fc-e0ee-4ecf-80d7-181dc53ea303 · outbound

This paper cites How useful is self- supervised pretraining for visual tasks? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7345–7354, 2020.

Automated Learning of Semantic Embedding Representations for Diffusion Models How useful is self- supervised pretraining for visual tasks? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7345–7354, 2020

Reference 26

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Observation 97cedc10-8c16-4059-9fe2-c3483a5ed713 · outbound

This paper cites Re- thinking pre-training and self-training.

Automated Learning of Semantic Embedding Representations for Diffusion Models Re- thinking pre-training and self-training

Reference 27

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This paper cites Diffusion au- toencoders: Toward a meaningful and decodable repre- sentation.

Automated Learning of Semantic Embedding Representations for Diffusion Models Diffusion au- toencoders: Toward a meaningful and decodable repre- sentation

Reference 28

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Observation 48d42c88-49b2-456a-b254-0a50afd2b543 · outbound

This paper cites Stacked denoising autoencoders: Learning useful rep- resentations in a deep network with a local denoising criterion.

Automated Learning of Semantic Embedding Representations for Diffusion Models Stacked denoising autoencoders: Learning useful rep- resentations in a deep network with a local denoising criterion

Reference 29

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This paper cites Masked autoencoders are scalable vision learners.

Automated Learning of Semantic Embedding Representations for Diffusion Models Masked autoencoders are scalable vision learners

Reference 30

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This paper cites Context autoencoder for self-supervised representation learning.

Automated Learning of Semantic Embedding Representations for Diffusion Models Context autoencoder for self-supervised representation learning

Reference 31

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Observation 5835ab41-baf3-44d6-9ce6-24dafd0b561f · outbound

This paper cites Mage: Masked gener- ative encoder to unify representation learning and image synthesis.

Automated Learning of Semantic Embedding Representations for Diffusion Models Mage: Masked gener- ative encoder to unify representation learning and image synthesis

Reference 32

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Observation 173dbcc7-afef-43ca-9eb5-b7b73c237b1a · outbound

This paper cites Masked image modeling with local multi-scale reconstruction.

Automated Learning of Semantic Embedding Representations for Diffusion Models Masked image modeling with local multi-scale reconstruction

Reference 33

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Observation ab0a47f9-a9f6-4727-9ab6-cf4387034ed4 · outbound

This paper cites Disjoint masking with joint distillation for efficient masked image modeling.

Automated Learning of Semantic Embedding Representations for Diffusion Models Disjoint masking with joint distillation for efficient masked image modeling

Reference 34

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Observation fb2d85c2-b299-4e9b-af7f-e0312ef763b7 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive archi- tecture.

Automated Learning of Semantic Embedding Representations for Diffusion Models Self-supervised learning from images with a joint-embedding predictive archi- tecture

Reference 35

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Observation c237295d-262b-413b-b1f2-3fa6ce917b61 · outbound

This paper cites A weakly supervised and globally explainable learning framework for brain tumor segmentation.

Automated Learning of Semantic Embedding Representations for Diffusion Models A weakly supervised and globally explainable learning framework for brain tumor segmentation

Reference 36

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Observation cdc2adef-0498-4292-9506-5b1b4f6f234e · outbound

This paper cites Scheduled de- noising autoencoders.

Automated Learning of Semantic Embedding Representations for Diffusion Models Scheduled de- noising autoencoders

Reference 37

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Observation a3c2f4be-90b7-44c5-b7ab-da02ad5bc923 · outbound

This paper cites Convolutional adaptive denoising autoencoders for hierarchical feature extrac- tion.

Automated Learning of Semantic Embedding Representations for Diffusion Models Convolutional adaptive denoising autoencoders for hierarchical feature extrac- tion

Reference 38

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Observation 0cd45af4-ba14-4a8c-a32a-cdeaf9fa6313 · outbound

This paper cites Understanding Diffusion Models: A Unified Perspective.

Automated Learning of Semantic Embedding Representations for Diffusion Models Understanding Diffusion Models: A Unified Perspective

Reference 39

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Observation f31cacb7-25ed-4b85-aec0-68fe2c1aee49 · outbound

This paper cites Classifier-free diffu- sion guidance.

Automated Learning of Semantic Embedding Representations for Diffusion Models Classifier-free diffu- sion guidance

Reference 40

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Observation ee9ce53c-b395-471f-a36c-6553b348e083 · outbound

This paper cites Self-supervised learning: Generative or contrastive.

Automated Learning of Semantic Embedding Representations for Diffusion Models Self-supervised learning: Generative or contrastive

Reference 41

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Observation fe24ebd6-f1e9-4d72-a510-0774acc2f90f · outbound

This paper cites Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference.

Automated Learning of Semantic Embedding Representations for Diffusion Models Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference

Reference 42

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Observation 44cd8c6f-3f34-4aa7-b8a8-80f684452e56 · outbound

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

Automated Learning of Semantic Embedding Representations for Diffusion Models All are worth words: A vit backbone for diffusion models

Reference 43

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Observation 54f94e76-7a85-4836-a04b-4ca869023acb · outbound

This paper cites Layercam: Exploring hierarchical class activation maps for localization.

Automated Learning of Semantic Embedding Representations for Diffusion Models Layercam: Exploring hierarchical class activation maps for localization

Reference 44

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Observation 34c91642-84da-4e32-8836-5d509c5d0d46 · outbound

This paper cites Scalable diffusion models with transformers.

Automated Learning of Semantic Embedding Representations for Diffusion Models Scalable diffusion models with transformers

Reference 45

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Observation bf35b5cb-d08c-42f0-92c7-662fdab29d11 · outbound

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

Automated Learning of Semantic Embedding Representations for Diffusion Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 46

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Observation a477816c-45de-445b-bcc5-08c29ab1af4d · outbound

This paper cites Scalelong: Towards more stable training of diffusion model via scaling network long skip connection.

Automated Learning of Semantic Embedding Representations for Diffusion Models Scalelong: Towards more stable training of diffusion model via scaling network long skip connection

Reference 47

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Observation 34ba7c52-5a8a-44b5-b020-84cb1bbb57fb · outbound

This paper cites Film: Visual reasoning with a general conditioning layer.

Automated Learning of Semantic Embedding Representations for Diffusion Models Film: Visual reasoning with a general conditioning layer

Reference 48

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Observation d5fdbab6-e5d6-48a9-b0d0-dd99646c3233 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].

Automated Learning of Semantic Embedding Representations for Diffusion Models The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 49

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Observation 478039c3-3a9a-41d5-ac70-10f8a6571059 · outbound

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

Automated Learning of Semantic Embedding Representations for Diffusion Models Learning multiple layers of features from tiny images

Reference 50

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Observation cf4afdb8-5d85-4a5b-883a-d84f9c01e2a5 · outbound

This paper cites Tiny imagenet visual recognition challenge.

Automated Learning of Semantic Embedding Representations for Diffusion Models Tiny imagenet visual recognition challenge

Reference 51

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Observation cd92252d-e81a-4e70-aec6-0fd7a51237d5 · outbound

This paper cites Labeled optical coherence tomography (oct) and chest x-ray images for classification.

Automated Learning of Semantic Embedding Representations for Diffusion Models Labeled optical coherence tomography (oct) and chest x-ray images for classification

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:08.826791Z

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Observation e681bf8f-fdd9-4b57-8188-4c00d5968262 · outbound

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

Automated Learning of Semantic Embedding Representations for Diffusion Models Swin transformer: Hierarchical vision transformer using shifted windows

Reference 53

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Observation ee7fd5f6-041c-4606-bec7-6216ab6220b2 · outbound

This paper cites Zero-shot text-to-image generation.

Automated Learning of Semantic Embedding Representations for Diffusion Models Zero-shot text-to-image generation

Reference 54

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This paper cites Visu- alizing data using t-sne.

Automated Learning of Semantic Embedding Representations for Diffusion Models Visu- alizing data using t-sne

Reference 55

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

Automated Learning of Semantic Embedding Representations for Diffusion Models Imagenet: A large-scale hierarchical image database

Reference 56

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This paper cites Adam: A Method for Stochastic Optimization.

Automated Learning of Semantic Embedding Representations for Diffusion Models Adam: A Method for Stochastic Optimization

Reference 57

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This paper cites Decoupled weight decay regularization.

Automated Learning of Semantic Embedding Representations for Diffusion Models Decoupled weight decay regularization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:08.770208Z

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