Paper Citation Record · LEDGER

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery

As of 10 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2507.04051.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.04051 v1

Coverage vector

measured 62 of 62 reference resolution

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measured 63 of 63 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T14:42:01.334822Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T14:47:03.347932Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 80c62f9a-c4cc-4f2b-a095-5d1a411da381 · outbound

This paper cites Generalized category discovery with decoupled prototypical network.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Generalized category discovery with decoupled prototypical network

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:20.003997Z

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Observation be4de4dd-d6b3-4e2a-a1e8-e8938b5fc0ad · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Emerg- ing properties in self-supervised vision transformers

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:19.730007Z

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Observation 8e51297e-6f00-48aa-995c-1bb72b9e2c0a · outbound

This paper cites Parametric information maxi- mization for generalized category discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Parametric information maxi- mization for generalized category discovery

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:19.516267Z

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Observation 0179b4fc-13ab-4193-a27a-b44ceff7c652 · outbound

This paper cites Text-to-image diffusion mod- els are zero shot classifiers.NeurIPS, 2024.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Text-to-image diffusion mod- els are zero shot classifiers.NeurIPS, 2024

Reference 4

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Observation 4861388c-cda4-457d-bd67-de16f0fe6d2b · outbound

This paper cites On-the-fly cate- gory discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery On-the-fly cate- gory discovery

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:19.009219Z

Source-reported events for the cited work

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Observation b1fb38e7-eeb4-489a-9d81-13dcbb26e304 · outbound

This paper cites Diffusion-based probabilistic un- certainty estimation for active domain adaptation.NeurIPS,.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diffusion-based probabilistic un- certainty estimation for active domain adaptation.NeurIPS,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:18.756703Z

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Observation d162ed97-b4a9-413f-a915-fa147a34d665 · outbound

This paper cites Diverse data augmentation with diffusions for effective test-time prompt tuning.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diverse data augmentation with diffusions for effective test-time prompt tuning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:18.540923Z

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Observation d826a0c4-bc77-4310-9dc6-4e57201b8649 · outbound

This paper cites A unified objective for novel class discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery A unified objective for novel class discovery

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:18.315817Z

Source-reported events for the cited work

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Observation 7da76288-af32-4b69-94cc-25cd4cfb5711 · outbound

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

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery DreamDA: Generative Data Augmentation with Diffusion Models

Reference 9

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Observation f9417917-ccfe-4270-846e-d0a19981cade · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 10

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Observation 30fce410-f8ef-45b8-9b41-eca3d80d5499 · outbound

This paper cites Learning to discover novel visual categories via deep transfer cluster- ing.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Learning to discover novel visual categories via deep transfer cluster- ing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:18.134874Z

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Observation c582aab2-070c-4778-876b-a369cb4d43c7 · outbound

This paper cites Autonovel: Automati- cally discovering and learning novel visual categories.IEEE TPAMI, 2021.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Autonovel: Automati- cally discovering and learning novel visual categories.IEEE TPAMI, 2021

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:17.826990Z

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Observation e3039c75-7f70-4d7b-a837-bdf4d678dcdd · outbound

This paper cites John Wiley & Sons, Inc., 1975.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery John Wiley & Sons, Inc., 1975

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:17.482537Z

Source-reported events for the cited work

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Observation b5251441-0a8b-4390-8f88-04f93ba1fe46 · outbound

This paper cites Is synthetic data from generative models ready for image recognition?.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Is synthetic data from generative models ready for image recognition?

Reference 14

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Observation 6d4fbd5e-afc3-4c1e-91ac-ec7b4e0c7f5e · outbound

This paper cites Denoising diffu- sion probabilistic models.NeurIPS, 2020.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Denoising diffu- sion probabilistic models.NeurIPS, 2020

Reference 15

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Observation d3f9ee2f-a393-43b4-b9b2-1c2c4b0c80b7 · outbound

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

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diffusemix: Label- preserving data augmentation with diffusion models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:17.240886Z

Source-reported events for the cited work

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Observation 66087554-248f-40de-a8b5-b3014930b3da · outbound

This paper cites Joint representation learning and novel category discovery on single-and multi-modal data.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Joint representation learning and novel category discovery on single-and multi-modal data

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:16.997108Z

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Observation 0c61d7fc-acb8-4334-94af-e1708b4ab2d7 · outbound

This paper cites Podia- 3d: Domain adaptation of 3d generative model across large domain gap using pose-preserved text-to-image diffusion.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Podia- 3d: Domain adaptation of 3d generative model across large domain gap using pose-preserved text-to-image diffusion

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:16.699174Z

Source-reported events for the cited work

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Observation 224411cd-e925-4919-bbca-bd7776c8fb26 · outbound

This paper cites Proxy anchor-based unsu- pervised learning for continuous generalized category dis- covery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Proxy anchor-based unsu- pervised learning for continuous generalized category dis- covery

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:16.441761Z

Source-reported events for the cited work

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Observation 4533c11d-4873-48d0-b064-7c24d34c2b21 · outbound

This paper cites 3d object representations for fine-grained categorization.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery 3d object representations for fine-grained categorization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:16.249270Z

Source-reported events for the cited work

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Observation 79835513-0156-4ede-a922-85cc9b61da0e · outbound

This paper cites The hungarian method for the assignment problem.Naval research logistics quarterly, 1955.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery The hungarian method for the assignment problem.Naval research logistics quarterly, 1955

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:15.999730Z

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Observation 73bab13b-4508-4c46-a698-1295150627fa · outbound

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

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diffusion models already have A semantic latent space

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:15.731666Z

Source-reported events for the cited work

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Observation 350dd361-901f-4a69-b8a5-07ce2411b39c · outbound

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

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Your diffusion model is secretly a zero-shot classifier

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:15.441681Z

Source-reported events for the cited work

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Observation 6fc721c7-009a-4829-b5be-000c6877bd50 · outbound

This paper cites Context-based and diversity-driven specificity in compositional zero-shot learning.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Context-based and diversity-driven specificity in compositional zero-shot learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:15.152102Z

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Observation 0a20761c-314d-44bd-80e2-3d30f7bef693 · outbound

This paper cites Novel class discovery for ultra-fine-grained visual categorization.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Novel class discovery for ultra-fine-grained visual categorization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:14.853782Z

Source-reported events for the cited work

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Observation df47c484-b619-4c5c-8cc4-14fbf0cd7ebf · outbound

This paper cites Pmgnet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning.Computer Vision and Image Understanding, 249: 104197, 2024.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Pmgnet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning.Computer Vision and Image Understanding, 249: 104197, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:14.703661Z

Source-reported events for the cited work

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Observation 97025a27-6974-41f1-9444-8461a230c050 · outbound

This paper cites Some methods for classification and analysis of multivariate observations.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Some methods for classification and analysis of multivariate observations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:14.567429Z

Source-reported events for the cited work

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Observation 1958582f-8f0b-4302-b6dc-096ed90e1847 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:03.431624Z

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Observation 5bb501fa-4fde-4d69-8210-a24c9c510626 · outbound

This paper cites Cats and dogs.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Cats and dogs

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:14.387409Z

Source-reported events for the cited work

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Observation 6566e612-900b-4616-b8e9-e7c3c7748897 · outbound

This paper cites Unsupervised domain adap- tation via domain-adaptive diffusion.IEEE TIP, 2024.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Unsupervised domain adap- tation via domain-adaptive diffusion.IEEE TIP, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:14.218591Z

Source-reported events for the cited work

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Observation bdf49bb0-1779-4c04-b0fd-79fad2dcebab · outbound

This paper cites Dynamic conceptional contrastive learning for generalized category discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Dynamic conceptional contrastive learning for generalized category discovery

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:14.080167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b4c0a5d-ee1b-4d69-98de-29622b3a8873 · outbound

This paper cites Federated generalized category discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Federated generalized category discovery

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:13.921901Z

Source-reported events for the cited work

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Observation f83a3c52-96f4-4a0e-a6ff-1ff1afaae9d0 · outbound

This paper cites On the momentum term in gradient descent learning algorithms.Neural networks, 1999.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery On the momentum term in gradient descent learning algorithms.Neural networks, 1999

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:13.771841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9781de53-195c-49ab-87d5-fabe524792b2 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Learn- ing transferable visual models from natural language super- vision

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:13.595263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b28e3d6-570c-496d-9021-10bb2028cf94 · outbound

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

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:04.408297Z

Source-reported events for the cited work

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Observation cac759c7-a9bf-46ba-9162-3030441069de · outbound

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

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery High-resolution image syn- thesis with latent diffusion models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:04.537713Z

Source-reported events for the cited work

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Observation 26c729fc-8a0f-42dc-86f3-19e64aabf792 · outbound

This paper cites The map equation.The European Physical Journal Special Top- ics, 2009.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery The map equation.The European Physical Journal Special Top- ics, 2009

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:13.425934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe46bf1e-021e-4396-9efa-68849ebd658f · outbound

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

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:04.817002Z

Source-reported events for the cited work

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Observation 866cf37a-bb0a-45e4-b3c7-0f1fda380af9 · outbound

This paper cites Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:13.153079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51895fc6-65f3-40bc-8abf-fc87ae72868e · outbound

This paper cites Animating rotation with quaternion curves.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Animating rotation with quaternion curves

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:12.866118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09d7be6e-14a4-48c1-a45c-2be0b997bd8f · outbound

This paper cites Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:04:08.251440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad38fcbd-c0f0-405c-ae97-dbd1f786fdfc · outbound

This paper cites Effective Data Augmentation With Diffusion Models.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Effective Data Augmentation With Diffusion Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:05.419193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2b46b72-91c8-479c-a5d0-ff4c8c9d5ec6 · outbound

This paper cites Test-time stain adaptation with diffusion models for histopathology image classification.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Test-time stain adaptation with diffusion models for histopathology image classification

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:12.638097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97f2a8af-5624-47f1-b06e-e2b882319e87 · outbound

This paper cites The inaturalist species classification and de- tection dataset.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery The inaturalist species classification and de- tection dataset

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:12.416789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 46bc823c-ec11-448e-90d6-0e899768f7b2 · outbound

This paper cites Generalized category discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Generalized category discovery

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:12.156459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7e3ffabf-8cf9-477d-a1dc-1368d837d1d0 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.Computation & Neural Systems Technical Report,.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery The caltech-ucsd birds-200-2011 dataset.Computation & Neural Systems Technical Report,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:11.889115Z

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Observation ed401a11-8064-46fc-ab18-3143c313c745 · outbound

This paper cites Videocomposer: Compositional video synthesis with motion controllability.NeurIPS, 2023.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Videocomposer: Compositional video synthesis with motion controllability.NeurIPS, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:11.655017Z

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Observation 9f2e9c02-d5b1-49dd-8e9e-7ed2020ce9a5 · outbound

This paper cites Enhance im- age classification via inter-class image mixup with diffusion model.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Enhance im- age classification via inter-class image mixup with diffusion model

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:11.361450Z

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Observation f562bbd3-7510-4d95-a389-958a8b2d2b6b · outbound

This paper cites Parametric classification for generalized category discovery: A baseline study.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Parametric classification for generalized category discovery: A baseline study

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:11.120012Z

Source-reported events for the cited work

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Observation c9c1cf19-07fe-4f3a-bd99-dec962ca15f8 · outbound

This paper cites Metagcd: Learning to continually learn in generalized cat- egory discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Metagcd: Learning to continually learn in generalized cat- egory discovery

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:10.849965Z

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Observation f579127e-1524-471b-9b6e-16d61340ee0a · outbound

This paper cites Learning to distin- guish samples for generalized category discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Learning to distin- guish samples for generalized category discovery

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:10.636275Z

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Observation 0c4de176-dd14-42b1-8c66-8a00aa86e98f · outbound

This paper cites Diffusion models and semi-supervised learners benefit mutually with few labels.NeurIPS, 2024.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diffusion models and semi-supervised learners benefit mutually with few labels.NeurIPS, 2024

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:10.342178Z

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Observation 8737357b-a56a-4ef9-88ad-620086ed9711 · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:06.787739Z

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Observation 396c1e01-357c-4ab7-841a-9e43a28a8afa · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:10.072988Z

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Observation 2c4ecc54-095d-4c20-a6f0-a838a0889318 · outbound

This paper cites mixup: Beyond 903 empirical risk minimization.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery mixup: Beyond 903 empirical risk minimization

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:09.799342Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3bb875e7-3b1b-4ea0-9766-810707e12571 · outbound

This paper cites Incremental general- ized category discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Incremental general- ized category discovery

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:09.596879Z

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Observation b9e7475f-ed7a-4771-9c45-d968e6663aa8 · outbound

This paper cites Pyramidal person re-identification via multi-loss dynamic training.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Pyramidal person re-identification via multi-loss dynamic training

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:09.369729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4d56ddc2-dee5-46b1-ba66-8a189ce247ee · outbound

This paper cites Textual knowledge matters: Cross-modality co- teaching for generalized visual class discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Textual knowledge matters: Cross-modality co- teaching for generalized visual class discovery

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:07.445985Z

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Observation c8add011-bab9-4d78-9f71-0f1bc9dd011a · outbound

This paper cites Prototypical hash encoding for on-the-fly fine- grained category discovery.NeurIPS, 2025.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Prototypical hash encoding for on-the-fly fine- grained category discovery.NeurIPS, 2025

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:09.096970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ceb82185-fd62-4e4a-9ac6-b2d57c5e0533 · outbound

This paper cites Neighborhood contrastive learn- ing for novel class discovery.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Neighborhood contrastive learn- ing for novel class discovery

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:08.808728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 05a0804e-4bc0-4a52-91a1-1f28d1a408ac · outbound

This paper cites Novel class discovery in chest x-rays via paired images and text.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Novel class discovery in chest x-rays via paired images and text

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:04:08.560625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97141dff-f77c-4606-9e25-ab8659dd7663 · outbound

This paper cites Training on Thin Air: Improve Image Classification with Generated Data.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Training on Thin Air: Improve Image Classification with Generated Data

Reference 62

Resolution: malformed identifier; no resolver link, observed 2026-08-06T20:04:07.964738Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8d6f15b2-8580-4531-a5a9-9531450e1ff7 · inbound

Identifying Latent Concepts and Structures for Generalized Category Discovery cites this paper.

Identifying Latent Concepts and Structures for Generalized Category Discovery Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-07-02T14:47:03.349247Z

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