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Paper Citation Record · LEDGER

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance

As of 17 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2505.11703.

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

pith.paper-citation-record.v1
2505.11703 v1

Coverage vector

measured 66 of 66 reference resolution

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measured 66 of 66 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

66 of 66 outbound references displayed

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External citation measurements

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Outbound references

Observation 70076c1b-d8be-4249-9a63-7fba4f9f3f28 · outbound

This paper cites Self-consuming gen- erative models go mad.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Self-consuming gen- erative models go mad

Reference 1

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Unresolved cited work

Reference 2

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Observation 93ea507b-2b19-4f06-99d8-e6e3a8900749 · outbound

This paper cites Leaving Reality to Imagination: Robust Classification via Generated Datasets.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Leaving Reality to Imagination: Robust Classification via Generated Datasets

Reference 3

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Observation 86c337d9-f70f-4886-b395-9032a2e44485 · outbound

This paper cites On the stability of iterative retraining of generative models on their own data.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance On the stability of iterative retraining of generative models on their own data

Reference 4

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Observation a0a1c8c7-a24e-4055-a6b0-1d1682123532 · outbound

This paper cites Improving image generation with better captions.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Improving image generation with better captions

Reference 5

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Observation 395cc0bf-1758-47a5-89e0-dac2267c5f8b · outbound

This paper cites PaliGemma: A versatile 3B VLM for transfer.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance PaliGemma: A versatile 3B VLM for transfer

Reference 6

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Observation da68096b-f9d5-4ab5-941d-a0c478f11e66 · outbound

This paper cites Food-101–mining discriminative components with random forests.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Food-101–mining discriminative components with random forests

Reference 7

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Observation 15ec1cb4-232d-4be0-912c-bb5f41e1aa0a · outbound

This paper cites An empiri- cal study of training self-supervised vision transformers.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance An empiri- cal study of training self-supervised vision transformers

Reference 8

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Observation 4c79e6aa-f379-424e-b9dc-b1d6bfe80798 · outbound

This paper cites Cimpoi, S.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Cimpoi, S

Reference 9

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This paper cites Turrisi da Costa, Nicola Dall’Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Turrisi da Costa, Nicola Dall’Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci

Reference 10

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Observation 45762109-6d49-49be-b964-5f78c9d17d9d · outbound

This paper cites Interpreting the Weight Space of Customized Diffusion Models.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Interpreting the Weight Space of Customized Diffusion Models

Reference 11

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Observation 81e69eeb-8b64-4d1b-99f6-b4b031673a9c · outbound

This paper cites Dream the impossible: Outlier imagination with diffusion models.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Dream the impossible: Outlier imagination with diffusion models

Reference 12

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Observation 634af2dc-54cd-4095-9385-ee03df976b67 · outbound

This paper cites Gonzalez, and Trevor Darrell.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Gonzalez, and Trevor Darrell

Reference 13

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This paper cites Scaling laws of synthetic images for model training.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Scaling laws of synthetic images for model training

Reference 14

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Observation 3c247fd9-c74a-4f40-bfe2-dca4c9e0df1f · outbound

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance DreamDA: Generative Data Augmentation with Diffusion Models

Reference 15

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Observation 6e50f182-3856-4a58-adfc-b760778c24e4 · outbound

This paper cites An image is worth one word: Personalizing text-to-image generation using textual inversion.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance An image is worth one word: Personalizing text-to-image generation using textual inversion

Reference 16

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Observation f189a9a1-a2f0-40c3-a29b-22b4c06021fe · outbound

This paper cites SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

Reference 17

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Observation cf5debda-e651-412b-85fb-ff963cc6c11f · outbound

This paper cites Deep residual learning for image recognition.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Deep residual learning for image recognition

Reference 18

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Observation ee8077fe-deae-4533-ace4-7226549d89df · outbound

This paper cites Is syn- thetic data from generative models ready for image recogni- tion? In ICLR, 2023.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Is syn- thetic data from generative models ready for image recogni- tion? In ICLR, 2023

Reference 19

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This paper cites Eurosat: A novel dataset and deep learn- ing benchmark for land use and land cover classification.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Eurosat: A novel dataset and deep learn- ing benchmark for land use and land cover classification

Reference 20

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Feedback-guided Data Synthesis for Imbalanced Classification

Reference 21

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 22

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Denoising diffu- sion probabilistic models

Reference 23

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Observation 42d3fd0e-f010-4b1e-82e8-09b749338daf · outbound

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance LoRA: Low-rank adaptation of large language models

Reference 24

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Datadream: Few-shot guided dataset generation

Reference 25

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance 3d object representations for fine-grained categorization

Reference 26

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Image Captions are Natural Prompts for Text-to-Image Models

Reference 27

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Caltech 101, 2022

Reference 28

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Ex- plore the power of synthetic data on few-shot object detec- tion

Reference 29

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This paper cites Does feasi- bility matter? understanding the impact of feasibility on syn- thetic training data.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Does feasi- bility matter? understanding the impact of feasibility on syn- thetic training data

Reference 30

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This paper cites Decoupled weight decay regularization.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Decoupled weight decay regularization

Reference 31

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Unresolved cited work

Reference 32

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 33

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LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Automated flower classification over a large number of classes

Reference 34

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Observation 2e3ba0c7-4347-4d33-b94c-cb0e68f085a4 · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Parkhi, Andrea Vedaldi, Andrew Zisserman, and C

Reference 35

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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.

source=pdf_text observed=2026-08-15T20:53:04.897378Z digest=sha256:0d9b3a83b22f8948b418908b96a6b2682d0c7df0047db58814cc640ae7376bc0

Observation 33fb6d28-44d5-4198-83ba-4527c45fe010 · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 36

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source=pdf_text observed=2026-08-15T20:53:04.901284Z digest=sha256:bc51cccdb89c82d1ce91f130a4bf1cad03a2fda2a9a0672c40e19c047d8bba62

Observation 7d6d1fda-c4f9-440e-b12c-0a0e273fd3cf · outbound

This paper cites Controlling text-to-image diffusion by orthogo- nal finetuning.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Controlling text-to-image diffusion by orthogo- nal finetuning

Reference 37

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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.

source=pdf_text observed=2026-08-15T20:53:04.904914Z digest=sha256:598b09ce490f8649df573cfbdd0465af59a5e0905c5aa0f4259ac7fde1c28dbd

Observation 25ad4f08-12d8-4ad7-a094-eeb1920268fb · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Learn- ing transferable visual models from natural language super- vision

Reference 38

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source=pdf_text observed=2026-08-15T20:53:04.908751Z digest=sha256:1c6e9644da5d1f313545b15d8b18e7676fbbb26b3808edbe344daa12fe9e6436

Observation 94701490-0b80-4af3-ae05-74c0e0187f46 · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 39

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source=pdf_text observed=2026-08-15T20:53:04.912732Z digest=sha256:edd6db36b7abcd0ef95ef6679176e94ffbdf4a3c3abebc7979f71cc8021c197f

Observation 702039bf-0e5c-4261-a25c-237d9e2560f6 · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance High-resolution image syn- thesis with latent diffusion models

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-15T20:53:05.468753Z

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.

source=pdf_text observed=2026-08-15T20:53:04.916459Z digest=sha256:2b0cb212e801a02ad1604b1dac751d59e39f1c67220ea8c0041eb695634feb67

Observation 494cc6d6-7b30-4f64-b1a0-0ac7e677c23b · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.920371Z digest=sha256:7b9252280fd496671e34342f3c13fe0fb1a4c438b8af473113464145372fa154

Observation f5c56aa1-7254-426b-b8c9-e0a3b3a863e4 · outbound

This paper cites Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models

Reference 42

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raw_fallback, observed 2026-08-15T20:53:05.448240Z

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.

source=pdf_text observed=2026-08-15T20:53:04.924281Z digest=sha256:7b1eec4eb6be5c0f8bbaee262af3725e5000e8e98edb87eaac3410497f706e7b

Observation c8adb78f-24e2-413b-bb21-fc9c23c94d66 · outbound

This paper cites Imagenet large scale visual recognition challenge.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Imagenet large scale visual recognition challenge

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.928283Z digest=sha256:8ce413c300fcb858823fad7c68505ed94dbc9362d8d487cb41ee0b7976cccfb7

Observation 02d39add-e894-46f4-a0f1-ae45b22725fa · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Photorealistic text-to-image diffusion models with deep language understanding

Reference 44

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source=pdf_text observed=2026-08-15T20:53:04.932031Z digest=sha256:89c659b41de2019441f4561cced3c792932fa716c37fcabb709f53b86628fd65

Observation d1a88023-4b31-4f94-a7f2-c31c351ee178 · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Fake it till you make it: Learning trans- ferable representations from synthetic imagenet clones

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-15T20:53:05.418836Z

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.

source=pdf_text observed=2026-08-15T20:53:04.935797Z digest=sha256:3e9b8e3eb0350bc9c7faf0b74e908914d7179a5b8b6d3c1fa8782877a33a7bf1

Observation 800043fb-8e38-4df4-baa3-8aec5c4a1828 · outbound

This paper cites Synth$^2$: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Synth$^2$: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings

Reference 46

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.939884Z digest=sha256:71ca3e95e9e8d6bf67f896332fd244f3f0b7173d91f640be6746a4636ce96f5c

Observation 922d5fcb-3c8b-4133-9ca7-9664fcfbec90 · outbound

This paper cites Instant- booth: Personalized text-to-image generation without test- time finetuning.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Instant- booth: Personalized text-to-image generation without test- time finetuning

Reference 47

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raw_fallback, observed 2026-08-15T20:53:05.405945Z

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.

source=pdf_text observed=2026-08-15T20:53:04.944148Z digest=sha256:7793ceca0dd9f2128b3202b334297d7c1761cffda796d036b5ede105fb2d4c65

Observation cababd4a-882d-45c4-98fe-c28f827eaef7 · outbound

This paper cites Fill-Up: Balancing Long-Tailed Data with Generative Models.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Fill-Up: Balancing Long-Tailed Data with Generative Models

Reference 48

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.948108Z digest=sha256:62a70ac8f3b1a6cdf4a549f2e9233f216bf35f0aacfa846767436f792ad07403

Observation 154dce92-0d79-4900-9814-e0f3183c0504 · outbound

This paper cites Diversity is definitely needed: 10 Improving model-agnostic zero-shot classification via stable diffusion, 2023.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Diversity is definitely needed: 10 Improving model-agnostic zero-shot classification via stable diffusion, 2023

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-15T20:53:05.393292Z

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.

source=pdf_text observed=2026-08-15T20:53:04.952244Z digest=sha256:49179e4b78d6356cbd585f18da214b24643a635849d2ce23f43980c7280693c1

Observation e9d721fe-2d37-40f1-9d77-b0bf7ee933b2 · outbound

This paper cites Semantic-aware data augmentation for text-to-image synthesis.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Semantic-aware data augmentation for text-to-image synthesis

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-15T20:53:05.380379Z

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.

source=pdf_text observed=2026-08-15T20:53:04.957113Z digest=sha256:2bfaaeb2af732605e74e3642c8c130b861e0ed61bd072a1c233a598a9255fc42

Observation 8a5fa428-9961-44cc-a27f-537a6b0b71dc · outbound

This paper cites Amu-tuning: Effective logit bias for clip-based few-shot learning.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Amu-tuning: Effective logit bias for clip-based few-shot learning

Reference 51

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raw_fallback, observed 2026-08-15T20:53:05.366248Z

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.

source=pdf_text observed=2026-08-15T20:53:04.961518Z digest=sha256:5a411ad7c22e0b61d465b48b8a5daa38787237ecc656f63d920373226837d1da

Observation ec6fbb9a-fff4-4e7c-84c2-924c56e04385 · outbound

This paper cites Stablerep: Synthetic images from text-to- image models make strong visual representation learners.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Stablerep: Synthetic images from text-to- image models make strong visual representation learners

Reference 52

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.965409Z digest=sha256:bbe6c66e096c468c0aa93083a3a6a26e47c31ea578c48d6106cc3fabc58807e3

Observation 29463b6d-6da0-45c6-9ce4-1a06c2b66a1e · outbound

This paper cites Learning vision from mod- els rivals learning vision from data.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Learning vision from mod- els rivals learning vision from data

Reference 53

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.969221Z digest=sha256:7225875ee8bc59f0262de37d21c2184347b9da00f7101d0b5ad8bd0b26dd9d85

Observation da1631ba-6971-40ac-9e63-fb03a87ecf25 · outbound

This paper cites Anti-dreambooth: Pro- tecting users from personalized text-to-image synthesis.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Anti-dreambooth: Pro- tecting users from personalized text-to-image synthesis

Reference 54

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raw_fallback, observed 2026-08-15T20:53:05.336956Z

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.

source=pdf_text observed=2026-08-15T20:53:04.973190Z digest=sha256:7ed0cd01e9cba0bfcd31351eca468c25f9250b7500fee6f6df2b522b867d71e6

Observation 1b6ea53b-9b72-4f79-ad16-e94d404d902b · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Sun database: Large-scale scene recognition from abbey to zoo

Reference 55

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.977012Z digest=sha256:25a3d87001c67fa96d5c8a59b682d7632f93cb65e4f0be335ad68b96531d4a80

Observation e471f688-1fee-4e16-91b7-78795466130f · outbound

This paper cites SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems

Reference 56

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.980967Z digest=sha256:c02e0f9736e148ebcbd6aa6cf27c57716a675d501bb3005502e8bcb81e70e7f3

Observation 165588f0-e299-458f-92ad-82e59fcc3cbc · outbound

This paper cites Controlled Training Data Generation with Diffusion Models.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Controlled Training Data Generation with Diffusion Models

Reference 57

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source=pdf_text observed=2026-08-15T20:53:04.984842Z digest=sha256:9493d8bb27c5518d3359953effdcba11e1604ac6d43bc943d8ef34241f1eecfc

Observation b727bcd6-f244-458b-b172-e7c12c574b74 · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Diffusion models and semi-supervised learners benefit mutually with few labels

Reference 58

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raw_fallback, observed 2026-08-15T20:53:05.315668Z

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.

source=pdf_text observed=2026-08-15T20:53:04.988948Z digest=sha256:01c413023ce013e40cc4df74f67f081e467bfbf89fe5e584beb6dd0d673f5346

Observation 27172c52-211f-4524-91ff-fb07fb9170c5 · outbound

This paper cites Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images

Reference 59

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:04.992833Z digest=sha256:3dcb8819a7f8f627a953ca9a6e75f49d5d72111e157303782a32440ac5492a53

Observation d128ec8c-37d7-4f34-b059-697364e83f86 · outbound

This paper cites Real-fake: Effective training data synthesis through distribution matching.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Real-fake: Effective training data synthesis through distribution matching

Reference 60

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raw_fallback, observed 2026-08-15T20:53:05.301619Z

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.

source=pdf_text observed=2026-08-15T20:53:04.996817Z digest=sha256:bd1d5716b1e8db26707781dc75624a4229f6d3ae32f5fe03cfd440a77dc83791

Observation 2ced5517-3a8d-4b83-8eb8-2e2fd8795a4e · outbound

This paper cites Diffmorpher: Unleashing the capability of diffu- sion models for image morphing.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Diffmorpher: Unleashing the capability of diffu- sion models for image morphing

Reference 61

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raw_fallback, observed 2026-08-15T20:53:05.288995Z

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.

source=pdf_text observed=2026-08-15T20:53:05.000727Z digest=sha256:54961d1bfd4a0617500067e23692ac176aa717d47ef22177228ae3ff6273b41d

Observation 632be87f-fd78-4027-9dd9-e3df649c78af · outbound

This paper cites Tip- adapter: Training-free adaption of clip for few-shot classi- fication.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Tip- adapter: Training-free adaption of clip for few-shot classi- fication

Reference 62

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raw_fallback, observed 2026-08-15T20:53:05.276461Z

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.

source=pdf_text observed=2026-08-15T20:53:05.004368Z digest=sha256:22037293c0c5922b00ae360fe0375d12fd597a9cacf72b5daa9ebbf7e825f927

Observation 60838e96-e510-48fc-8d35-dd10d02bf59f · outbound

This paper cites Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners

Reference 63

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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.

source=pdf_text observed=2026-08-15T20:53:05.008307Z digest=sha256:2dfb14b705795f8e8fee32cdaa873c2bab45908c5afccd85c14a4dfa70150255

Observation eb3f13e5-0957-41b7-b9f3-0b9bdbd0f567 · outbound

This paper cites Toward understanding generative data augmentation.NeurIPS, 2023.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Toward understanding generative data augmentation.NeurIPS, 2023

Reference 64

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raw_fallback, observed 2026-08-15T20:53:05.249570Z

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.

source=pdf_text observed=2026-08-15T20:53:05.012137Z digest=sha256:b702f44a47eed85e01b8991aa79d0948952ab8ca55180d5085a0a0db596e2f2c

Observation f089ea6f-bf94-41a5-bafa-93792fbc4eac · outbound

This paper cites Learning to prompt for vision-language models.

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Learning to prompt for vision-language models

Reference 65

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:05.016175Z digest=sha256:25b45b87ae8ff8e3be85e345072b189040251be9d6f158a684c5abb4a30d874f

Observation f060881f-da87-4c79-b29d-4d91993eb39b · outbound

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

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance Training on Thin Air: Improve Image Classification with Generated Data

Reference 66

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:05.020228Z digest=sha256:ab2e8a1f2c191ae81cc340df1a09195817000474ef83fa3ed61252932753cff4

Pith citing papers

No inbound Pith citation observations are available.