HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation | Paper Citation Record

Observation fa9c6298-2e20-40e3-a32d-c53a00c04b41 · outbound

This paper cites Data Augmentation Generative Adversarial Networks.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Data Augmentation Generative Adversarial Networks

Reference 1

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This paper cites an unresolved cited work.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unresolved cited work

Reference 2

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Observation 2ee9c9d2-6872-4689-85aa-b3f3b3174aad · outbound

This paper cites Stochastic gradient descent on riemannian manifolds.IEEE Transactions on Automatic Control, 58(9): 2217–2229, 2013.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Stochastic gradient descent on riemannian manifolds.IEEE Transactions on Automatic Control, 58(9): 2217–2229, 2013

Reference 3

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Observation 0b3cd34f-75ad-445e-8bb6-41bca786de28 · outbound

This paper cites Riemannian adaptive optimization methods.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Riemannian adaptive optimization methods

Reference 4

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This paper cites Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and edit- ing.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and edit- ing

Reference 5

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Observation c75441c3-d275-404f-9b60-35898e4ecfe3 · outbound

This paper cites Hyperbolic graph convolutional neural networks.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Hyperbolic graph convolutional neural networks

Reference 6

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HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unresolved cited work

Reference 7

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Observation c9f929e7-3087-47ea-8b74-6a2ae6a7dc21 · outbound

This paper cites FIGR: Few-shot Image Generation with Reptile.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation FIGR: Few-shot Image Generation with Reptile

Reference 8

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Observation 91a525ee-ba5f-476c-a2b5-62f0fdcacc12 · outbound

This paper cites Learning joint latent space ebm prior model for multi-layer generator.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Learning joint latent space ebm prior model for multi-layer generator

Reference 9

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This paper cites an unresolved cited work.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unresolved cited work

Reference 10

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Observation 55ff6b51-5646-4a60-9282-9147b5dcb6a1 · outbound

This paper cites Hyper- bolic image-text representations.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Hyper- bolic image-text representations

Reference 11

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Observation 06fbe0b1-1dd2-4713-bbc9-dbc2b38ec6bc · outbound

This paper cites Embedding Text in Hyperbolic Spaces.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Embedding Text in Hyperbolic Spaces

Reference 12

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Observation 0e94437f-3b63-4407-a04e-4fe52b28d4c0 · outbound

This paper cites Attribute group editing for reliable few-shot image generation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Attribute group editing for reliable few-shot image generation

Reference 13

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Observation c09a93c3-0a64-4c00-9548-e421dfee9542 · outbound

This paper cites Stable Attribute Group Editing for Reliable Few-shot Image Generation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Stable Attribute Group Editing for Reliable Few-shot Image Generation

Reference 14

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Observation 92160485-cb8f-466a-9342-2aa64d36db2f · outbound

This paper cites DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter

Reference 15

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Observation d23ed187-1807-4800-b637-dfa091a7334f · outbound

This paper cites Hyperbolic neural networks.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Hyperbolic neural networks

Reference 16

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Observation ae6a7461-0699-476d-be60-6b1bf4074e98 · outbound

This paper cites Hyperbolic contrastive learning for visual representations beyond objects.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Hyperbolic contrastive learning for visual representations beyond objects

Reference 17

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Observation e07552d4-cce3-422d-a631-b80457cdc507 · outbound

This paper cites Hyperbolic groups.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Hyperbolic groups

Reference 18

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Observation d7a3f593-15d4-4a96-ab87-08559e52acd1 · outbound

This paper cites Lofgan: Fusing local representations for fewshot image gen- eration.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Lofgan: Fusing local representations for fewshot image gen- eration

Reference 19

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Observation 9614bca2-c2dc-4010-8882-51d86f0ea18c · outbound

This paper cites Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion

Reference 20

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Observation 9a28adf5-e4b6-43a1-8590-df3b0e6aa9ee · outbound

This paper cites Svdiff: Compact parameter space for diffusion fine-tuning.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Svdiff: Compact parameter space for diffusion fine-tuning

Reference 21

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Observation 9af1319f-b512-4861-8e91-dc5f4cc1784c · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 22

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Observation cecfd2a4-33fb-4b11-8063-3150c88dd12d · outbound

This paper cites Classifier-free diffusion guidance.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Classifier-free diffusion guidance

Reference 23

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Observation 7a1dae38-5602-40e0-8a31-55b3fe6396de · outbound

This paper cites Denoising dif- fusion probabilistic models.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Denoising dif- fusion probabilistic models

Reference 24

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Observation d2671d80-3dc5-424f-854f-7aa961f79a6f · outbound

This paper cites Deltagan: Towards diverse few-shot image generation with sample-specific delta.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Deltagan: Towards diverse few-shot image generation with sample-specific delta

Reference 25

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Observation d92354d4-6717-4c4f-b395-583d2fbac632 · outbound

This paper cites Match- inggan: Matching-based few-shot image generation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Match- inggan: Matching-based few-shot image generation

Reference 26

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Observation 7537c663-a325-4d97-b4d4-c6cdb4e8c2f0 · outbound

This paper cites F2gan: Fusing-and-filling gan for few- shot image generation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation F2gan: Fusing-and-filling gan for few- shot image generation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.675278Z

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Observation 9657ba9c-c89a-4a3a-82b5-8a52c6fc969c · outbound

This paper cites Deltagan: Towards diverse few-shot image generation with sample-specific delta.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Deltagan: Towards diverse few-shot image generation with sample-specific delta

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.667125Z

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Observation 28ec9ed0-8637-4a88-a960-c1d5523c99e8 · outbound

This paper cites Few-shot image generation using discrete content representation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Few-shot image generation using discrete content representation

Reference 29

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Observation e92cb39a-1d82-46a0-a11b-f25131cb9244 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation A style-based generator architecture for generative adversarial networks

Reference 30

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Observation e131fce9-1f55-40dc-ac54-fc22d11a4cad · outbound

This paper cites Hyperbolic image embeddings.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Hyperbolic image embeddings

Reference 31

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Observation 8de4ae39-83fb-4b27-a86c-7e7cfae001e0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Adam: A Method for Stochastic Optimization

Reference 32

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Observation d3cb76cb-b7d9-46d9-8805-6fa343f00df7 · outbound

This paper cites Multi-concept customization of text- to-image diffusion.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Multi-concept customization of text- to-image diffusion

Reference 33

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Observation c7ed10ec-926c-4765-8be9-6007efd27593 · outbound

This paper cites Riemannian manifolds: an introduction to cur- vature.Springer Science & Business Media, 176, 2006.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Riemannian manifolds: an introduction to cur- vature.Springer Science & Business Media, 176, 2006

Reference 34

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Observation 83d728b4-f38d-4777-b2e2-edd4fe2f3eba · outbound

This paper cites Springer,.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Springer,

Reference 35

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Observation fdfc7c3f-381f-48a5-ae6d-7f389cca16c3 · outbound

This paper cites Hypersdfusion: Bridging hierarchical structures in language and geometry for enhanced 3d text2shape genera- tion.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Hypersdfusion: Bridging hierarchical structures in language and geometry for enhanced 3d text2shape genera- tion

Reference 36

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Observation a86bd40d-49e0-4e67-bce9-e9906dfb07cd · outbound

This paper cites The euclidean space is evil: Hyperbolic attribute editing for few-shot image generation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation The euclidean space is evil: Hyperbolic attribute editing for few-shot image generation

Reference 37

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Observation af34a7dc-fc66-4255-9dfa-41fd2df581d6 · outbound

This paper cites DAWSON: A Domain Adaptive Few Shot Generation Framework.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation DAWSON: A Domain Adaptive Few Shot Generation Framework

Reference 38

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Observation 173f271e-4626-4979-a25f-9f19fe052bc3 · outbound

This paper cites Few-shot unsueprvised image-to-image translation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Few-shot unsueprvised image-to-image translation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.594229Z

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Observation fc052684-b4ca-4d49-9e57-3b99bc55537e · outbound

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

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Umap: Uniform manifold approximation and projection.The Journal of Open Source Software, 3(29):861,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.586373Z

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Observation b9d221ba-bfb9-4fb0-874e-43aba12f02ec · outbound

This paper cites Generative visual manipulation on the natural image manifold.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Generative visual manipulation on the natural image manifold

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.578260Z

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Observation 602baf83-3205-4696-991b-c9c838e13b69 · outbound

This paper cites Learning continuous hierarchies in the lorentz model of hyperbolic geometry.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Learning continuous hierarchies in the lorentz model of hyperbolic geometry

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.569954Z

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Observation 8887bebb-98b4-4d99-ae07-1e9b802ca43e · outbound

This paper cites Automated flower classification over a large number of classes.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Automated flower classification over a large number of classes

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.561308Z

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Observation 62e1d664-d50b-4333-966c-a53da10fc213 · outbound

This paper cites Unsupervised hyperbolic representation learning via message passing auto-encoders.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unsupervised hyperbolic representation learning via message passing auto-encoders

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.552386Z

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Observation 133c9b91-feb3-4e9d-9251-8c3476d2e8d3 · outbound

This paper cites Parkhi, Andrea Vedaldi, and Andrew Zisserman.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Parkhi, Andrea Vedaldi, and Andrew Zisserman

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.544019Z

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Observation 8cd749d3-ca53-4df4-b575-1da6c92b8f09 · outbound

This paper cites Moment matching for multi-source domain adaptation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Moment matching for multi-source domain adaptation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.535461Z

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Observation c7d2aaa1-c24c-4068-9ef4-5e18cf715f99 · outbound

This paper cites Diffusion autoencoders: Toward a meaningful and decodable representation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Diffusion autoencoders: Toward a meaningful and decodable representation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.526409Z

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Observation 1ccaee1b-1bf4-4201-9298-1e98f9cb40f9 · outbound

This paper cites Learning transferable visual models from natural language supervision.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Learning transferable visual models from natural language supervision

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.517641Z

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Observation c4689f9e-3a80-4607-b492-cb40e9016190 · outbound

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

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 49

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Observation 9a173d4c-4e2d-4113-9507-598bc9cc1834 · outbound

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

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation High-resolution image synthesis with latent diffusion models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.508663Z

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Observation cb63edb4-a0a2-4262-93e0-7d47d84f995c · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven gen- eration.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Dreambooth: Fine tuning text-to-image diffusion models for subject-driven gen- eration

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.500123Z

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Observation 6a40ecbf-5f4a-4fc9-86d6-a2c56943ee00 · outbound

This paper cites In- stantbooth: Personalized text-to-image generation without test-time finetuning.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation In- stantbooth: Personalized text-to-image generation without test-time finetuning

Reference 52

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Observation 79741d00-2c74-4b56-b59a-b1f4917e1208 · outbound

This paper cites Denoising diffusion implicit models.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Denoising diffusion implicit models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.483180Z

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Observation 2b2400a6-bd54-40ab-a3b5-66502c82aee3 · outbound

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

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Score-based generative modeling through stochastic differential equations

Reference 54

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Observation 69842a3c-cf71-4531-8591-4f4d1da517f5 · outbound

This paper cites Learning the predictability of the future.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Learning the predictability of the future

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.469963Z

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Observation 34eb8a50-7bdd-4dc1-bbfd-4fdefe3082db · outbound

This paper cites Improved Vector Quantized Diffusion Models.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Improved Vector Quantized Diffusion Models

Reference 56

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Observation 9724a1f7-4036-45b2-afcc-ee2ad5c27e38 · outbound

This paper cites Poincaré glove: Hyperbolic word embeddings.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Poincaré glove: Hyperbolic word embeddings

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.461106Z

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Observation 2a720fdd-0ee9-4ea2-b96b-a160f6a97c22 · outbound

This paper cites Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.452858Z

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Observation 3c4edbc3-9024-4fbf-8130-ef5494d899d6 · outbound

This paper cites P+: Extended Textual Conditioning in Text-to-Image Generation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation P+: Extended Textual Conditioning in Text-to-Image Generation

Reference 59

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Observation 7796e9be-909a-4678-81df-ade7294517d9 · outbound

This paper cites Paint by ex- ample: Exemplar-based image editing with diffusion models.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Paint by ex- ample: Exemplar-based image editing with diffusion models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.444450Z

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Observation 6541cb56-3242-4fdb-9f2e-3a643f6ba0ed · outbound

This paper cites Wavegan: Frequency-aware gan for high-fidelity few-shot image generation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Wavegan: Frequency-aware gan for high-fidelity few-shot image generation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.435818Z

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Observation bf22bc64-c052-4f4b-87b3-6941c09079c5 · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 62

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This paper cites Adding conditional control to text-to-image diffusion models.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Adding conditional control to text-to-image diffusion models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.427252Z

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Observation 1d6ec134-a2b0-482b-be63-28c0cdc248b2 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation The unreasonable effectiveness of deep features as a perceptual metric

Reference 64

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This paper cites Where is my spot? few-shot image generation via latent subspace optimization.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Where is my spot? few-shot image generation via latent subspace optimization

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.413989Z

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Observation 5e9eb4b5-eda7-4a5d-99ac-7d4da327263e · outbound

This paper cites Exact fusion via feature distribution match- ing for few-shot image generation.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Exact fusion via feature distribution match- ing for few-shot image generation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.406030Z

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HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:42:51.397922Z

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Observation 928bedbe-8059-4021-af8d-75e70d6a0a4a · outbound

This paper cites forward process.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation forward process

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.382119Z

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Observation 36b41b66-6ae6-4125-b2db-df03cf1b778d · outbound

This paper cites children.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation children

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.374406Z

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Observation 13b4e553-0689-4064-9df7-e30d1855c61a · outbound

This paper cites parent” im- ages,HypDAEcontrols the semantic diversity of gener- ated images (Fig. 24), where the “parent.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation parent” im- ages,HypDAEcontrols the semantic diversity of gener- ated images (Fig. 24), where the “parent

Reference 71

Resolution: verified exact; raw_fallback, observed 2026-08-12T11:42:51.174377Z

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Observation 34df5ac5-b2e1-453e-b876-93879c8d0ab9 · outbound

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HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:42:51.366642Z

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Observation e3f95c16-2767-4de0-8473-e6a4ef2dff05 · outbound

This paper cites painting.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation painting

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.358799Z

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Observation c5eacbe3-729c-4df2-9523-c73d8ccbd0fc · outbound

This paper cites children.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation children

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.350812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5e1ac32d-0ab6-451f-a402-fd54f7cccc8c · outbound

This paper cites As shown in Fig.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation As shown in Fig

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.342899Z

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Observation 61fd36f9-ab97-437e-a2c1-8334ff55fa87 · outbound

This paper cites Results are shown in the main text.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Results are shown in the main text

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.334621Z

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Observation fa395a03-73b0-49c2-997e-59936db0f7ae · outbound

This paper cites Overall, there were 20 original images and 60 generated variants in total.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Overall, there were 20 original images and 60 generated variants in total

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.326526Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a450da76-917d-4b73-9b38-7098db1c8b95 · outbound

This paper cites We then shuffled the orders for all images.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation We then shuffled the orders for all images

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.317965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a6922f7-ade8-481b-bcdc-85a48ae5dd45 · outbound

This paper cites Fidelity.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Fidelity

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.308379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b889a7cf-2f60-43ae-8ef1-cd8883b41b28 · outbound

This paper cites 28 and Fig.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation 28 and Fig

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.299748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64b8432a-4810-4026-9d7a-bf7bf4dbedc4 · outbound

This paper cites an unresolved cited work.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:42:51.291081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68f9e583-2da7-423e-9efd-5452695686f1 · outbound

This paper cites an unresolved cited work.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:42:51.282283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ea3b210-3721-4960-b182-01369343fddb · outbound

This paper cites an unresolved cited work.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:42:51.273617Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e110d4da-c690-4648-9ade-1bc9e4bf4b63 · outbound

This paper cites (7) in the main paper is selected as 0.1.

HypDAE: Hyperbolic Diffusion Autoencoders for Hierarchical Few-shot Image Generation (7) in the main paper is selected as 0.1

Reference 256

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:42:51.390019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Outbound references

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