LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance | Paper Citation Record

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

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

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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

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

Reference 5

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

Reference 7

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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

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

Reference 12

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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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Observation 5bda285f-aaf2-4a8a-bb81-8ae1a0f52df4 · outbound

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

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

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

Reference 15

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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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Observation 2fd39d07-ad19-49b1-9763-13cabd9db94d · outbound

This paper cites Feedback-guided Data Synthesis for Imbalanced Classification.

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

Reference 21

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Observation 3420ba80-0db6-46f0-b054-c1cf2dfcae4f · outbound

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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This paper cites Denoising diffu- sion probabilistic models.

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

This paper cites LoRA: Low-rank adaptation of large language models.

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

Reference 24

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Observation 7cc8ca14-f8f3-470c-b1bf-a3f10d0b0bcb · outbound

This paper cites Datadream: Few-shot guided dataset generation.

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

Reference 25

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Observation 8ab087e5-b77b-424c-b70c-a2032e9e969f · outbound

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

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

Reference 26

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Observation 3330322b-5bd3-4116-b5c8-c7a8698d2763 · outbound

This paper cites Image Captions are Natural Prompts for Text-to-Image Models.

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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Observation b561c00b-5dc0-4208-8f3d-3c5b3414f6d0 · outbound

This paper cites Caltech 101, 2022.

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

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Observation 2207f15e-0b50-4cdc-a68c-919145c4635b · outbound

This paper cites Ex- plore the power of synthetic data on few-shot object detec- tion.

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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Observation 6486b04e-2f17-49f9-8ec1-758560f2eac5 · outbound

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

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

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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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

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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

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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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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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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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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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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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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

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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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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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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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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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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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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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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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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

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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

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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

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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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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:05.336956Z

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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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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:05.315668Z

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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:05.301619Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:05.288995Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:05.276461Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:05.263330Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:05.249570Z

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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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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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