FACT: A Simple and Efficient Framework for Active Finetuning | Paper Citation Record

Observation ffb049f7-e18b-42bf-bbf0-3467819d65ae · outbound

This paper cites Bridging the gap between pre-training and fine-tuning for end-to-end speech translation,.

FACT: A Simple and Efficient Framework for Active Finetuning Bridging the gap between pre-training and fine-tuning for end-to-end speech translation,

Reference 1

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Observation f4786e8c-77dc-4018-8518-9575cd268523 · outbound

This paper cites Code: contrastive pre-training with adversarial fine-tuning for zero-shot expert linking,.

FACT: A Simple and Efficient Framework for Active Finetuning Code: contrastive pre-training with adversarial fine-tuning for zero-shot expert linking,

Reference 2

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Observation 07fd75b0-5fc8-4b2f-8717-920e91f92d4a · outbound

This paper cites Equi-tuning: Group equivariant fine-tuning of pretrained models,.

FACT: A Simple and Efficient Framework for Active Finetuning Equi-tuning: Group equivariant fine-tuning of pretrained models,

Reference 3

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Observation c02c945c-9d55-4173-9cf4-c916f08d76d7 · outbound

This paper cites Understanding Uncertainty Sampling via Equivalent Loss.

FACT: A Simple and Efficient Framework for Active Finetuning Understanding Uncertainty Sampling via Equivalent Loss

Reference 4

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Observation 0101283b-54b2-4000-b271-a51cdba13e81 · outbound

This paper cites Learning loss for active learning,.

FACT: A Simple and Efficient Framework for Active Finetuning Learning loss for active learning,

Reference 5

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Observation d1c55536-96a1-46eb-b81a-2bfd1304cfd5 · outbound

This paper cites Deep batch active learning by diverse, uncertain gradient lower bounds,.

FACT: A Simple and Efficient Framework for Active Finetuning Deep batch active learning by diverse, uncertain gradient lower bounds,

Reference 6

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Observation d25d0d3e-db9d-4b5e-ac08-742b9db302c6 · outbound

This paper cites Task-aware variational adversarial active learning,.

FACT: A Simple and Efficient Framework for Active Finetuning Task-aware variational adversarial active learning,

Reference 7

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Observation 4da1c96e-3988-4e89-84b7-8582106830a5 · outbound

This paper cites Active learning by feature mixing,.

FACT: A Simple and Efficient Framework for Active Finetuning Active learning by feature mixing,

Reference 8

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Observation fc6fc095-42d0-4252-bbc8-04169d02a3ed · outbound

This paper cites Deep active learning with noise stability,.

FACT: A Simple and Efficient Framework for Active Finetuning Deep active learning with noise stability,

Reference 9

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Observation 7366bfd0-b498-4535-919a-95c36e596335 · outbound

This paper cites Re- ducing label effort: Self-supervised meets active learning,.

FACT: A Simple and Efficient Framework for Active Finetuning Re- ducing label effort: Self-supervised meets active learning,

Reference 10

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Observation 687a74fa-4826-4f5c-ac37-757e6fc8c6df · outbound

This paper cites Active finetuning: Exploiting annotation budget in the pretraining-finetuning paradigm,.

FACT: A Simple and Efficient Framework for Active Finetuning Active finetuning: Exploiting annotation budget in the pretraining-finetuning paradigm,

Reference 11

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Observation f487a267-c6c8-48e0-84a8-9cce8c6f0d5c · outbound

This paper cites Activedc: Distribution calibration for active finetuning,.

FACT: A Simple and Efficient Framework for Active Finetuning Activedc: Distribution calibration for active finetuning,

Reference 12

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Observation 5b3eb848-42d1-41b7-a9b7-5a5ef9d55670 · outbound

This paper cites Boundary matters: A bi-level active finetuning method,.

FACT: A Simple and Efficient Framework for Active Finetuning Boundary matters: A bi-level active finetuning method,

Reference 13

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Observation a921f9e1-52ec-4caa-8b88-4d301fb1c5f8 · outbound

This paper cites Vecaf: Vision-language collaborative active finetuning with training objective awareness,.

FACT: A Simple and Efficient Framework for Active Finetuning Vecaf: Vision-language collaborative active finetuning with training objective awareness,

Reference 14

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Observation 2796a49d-6ccb-43b8-98e8-3fd14c2a3850 · outbound

This paper cites Fine-tuning can distort pretrained features and underperform out-of- distribution,.

FACT: A Simple and Efficient Framework for Active Finetuning Fine-tuning can distort pretrained features and underperform out-of- distribution,

Reference 15

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Observation ca41aa67-aaa4-44c4-a656-6ba665ef9525 · outbound

This paper cites Revisit finetuning strategy for few-shot learning to transfer the emdeddings,.

FACT: A Simple and Efficient Framework for Active Finetuning Revisit finetuning strategy for few-shot learning to transfer the emdeddings,

Reference 16

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Observation ae4bc505-b9cd-4d1b-8cbc-9f3d15b62613 · outbound

This paper cites An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning.

FACT: A Simple and Efficient Framework for Active Finetuning An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning

Reference 17

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Observation 4e853147-90d8-4c38-bb3c-ab5063820111 · outbound

This paper cites Deep neural networks for high dimension, low sample size data.

FACT: A Simple and Efficient Framework for Active Finetuning Deep neural networks for high dimension, low sample size data

Reference 18

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Observation 6735241b-a6bb-48f8-a41c-7f17db6a5267 · outbound

This paper cites Pushing the limits of simple pipelines for few-shot learning: External data and fine- tuning make a difference,.

FACT: A Simple and Efficient Framework for Active Finetuning Pushing the limits of simple pipelines for few-shot learning: External data and fine- tuning make a difference,

Reference 19

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Observation 84e8ef2f-a60e-4b20-9d52-6191296c4e10 · outbound

This paper cites Active learning on a budget: Opposite strategies suit high and low budgets,.

FACT: A Simple and Efficient Framework for Active Finetuning Active learning on a budget: Opposite strategies suit high and low budgets,

Reference 20

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Observation 561028df-8141-44f1-9986-82633b0bfe15 · outbound

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

FACT: A Simple and Efficient Framework for Active Finetuning Learning transferable visual models from natural language supervision,

Reference 21

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Observation c06a9335-210e-4a9d-a18f-de4a269633a0 · outbound

This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

FACT: A Simple and Efficient Framework for Active Finetuning Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 22

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Observation b50dfa44-b1e5-4a12-ae20-a5be4e1572eb · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale,.

FACT: A Simple and Efficient Framework for Active Finetuning An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation b0149390-8a1a-428f-8171-d5c39d13b649 · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

FACT: A Simple and Efficient Framework for Active Finetuning Training data-efficient image transformers & distillation through attention,

Reference 24

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Observation f352a3c8-b49d-46ad-b9d1-3f6f3a48e490 · outbound

This paper cites A convnet for the 2020s,.

FACT: A Simple and Efficient Framework for Active Finetuning A convnet for the 2020s,

Reference 25

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Observation 198fe951-87c4-4541-8dc3-5c62f23a7e5b · outbound

This paper cites Vision-LSTM: xLSTM as generic vision backbone,.

FACT: A Simple and Efficient Framework for Active Finetuning Vision-LSTM: xLSTM as generic vision backbone,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 929331ef-197b-47a2-b8ca-d1743f9dfaf0 · outbound

This paper cites A closer look at few-shot classification,.

FACT: A Simple and Efficient Framework for Active Finetuning A closer look at few-shot classification,

Reference 27

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Observation 43ac43c1-586f-4d16-8959-68b75c616de7 · outbound

This paper cites Charting the right manifold: Manifold mixup for few-shot learning,.

FACT: A Simple and Efficient Framework for Active Finetuning Charting the right manifold: Manifold mixup for few-shot learning,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation c564cdbd-26e1-4df9-a6fb-497db77f8b3d · outbound

This paper cites Autoaug- ment: Learning augmentation strategies from data,.

FACT: A Simple and Efficient Framework for Active Finetuning Autoaug- ment: Learning augmentation strategies from data,

Reference 29

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Observation 4b3463e6-1c46-4b39-8653-172eb3c0a818 · outbound

This paper cites Trivialaugment: Tuning-free yet state-of- the-art data augmentation,.

FACT: A Simple and Efficient Framework for Active Finetuning Trivialaugment: Tuning-free yet state-of- the-art data augmentation,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation c4e3d236-0520-4d4b-8bd9-a088ad0c1f66 · outbound

This paper cites Partial is better than all: Revisiting fine-tuning strategy for few-shot learning,.

FACT: A Simple and Efficient Framework for Active Finetuning Partial is better than all: Revisiting fine-tuning strategy for few-shot learning,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation ac2cf820-5124-45c6-b6d3-3008f44497e4 · outbound

This paper cites LoRA+: Efficient low rank adaptation of large models,.

FACT: A Simple and Efficient Framework for Active Finetuning LoRA+: Efficient low rank adaptation of large models,

Reference 32

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Observation 423cf796-4eea-400a-9c21-d5e6ad618682 · outbound

This paper cites LoRA-GA: Low-Rank Adaptation with Gradient Approximation.

FACT: A Simple and Efficient Framework for Active Finetuning LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:16:17.174886Z

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Observation 7368ac41-449d-484b-a490-d9d9478950c4 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

FACT: A Simple and Efficient Framework for Active Finetuning Qlora: Efficient finetuning of quantized llms,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation fae049c5-e9ce-4c71-b261-630c799350ca · outbound

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

FACT: A Simple and Efficient Framework for Active Finetuning LoRA: Low-rank adaptation of large language models,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 106c3829-6139-4d35-9914-dc0605154560 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

FACT: A Simple and Efficient Framework for Active Finetuning Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-01T22:16:17.182888Z

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Observation 9e2f5b80-f451-4ba0-9467-0be8ba2698b6 · outbound

This paper cites Visual prompt tuning,.

FACT: A Simple and Efficient Framework for Active Finetuning Visual prompt tuning,

Reference 37

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Observation 81b32901-4464-4dd2-8d71-05fbc9978f5a · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation,.

FACT: A Simple and Efficient Framework for Active Finetuning Prefix-tuning: Optimizing continuous prompts for generation,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 42059ced-bac1-4b1c-88f3-8681e389d2dd · outbound

This paper cites Parameter- efficient fine-tuning in spectral domain for point cloud learning,.

FACT: A Simple and Efficient Framework for Active Finetuning Parameter- efficient fine-tuning in spectral domain for point cloud learning,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 8d5ea37f-c86e-4e96-a8bd-fbad18c4e313 · outbound

This paper cites Finetune like you pretrain: Improved finetuning of zero-shot vision models,.

FACT: A Simple and Efficient Framework for Active Finetuning Finetune like you pretrain: Improved finetuning of zero-shot vision models,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 16edb30c-584d-464b-b988-f6e351765508 · outbound

This paper cites Frozen feature augmentation for few-shot image classification,.

FACT: A Simple and Efficient Framework for Active Finetuning Frozen feature augmentation for few-shot image classification,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 5c464315-76d9-43ad-aa80-7531e7f2bc59 · outbound

This paper cites Big transfer (bit): General visual representation learning,.

FACT: A Simple and Efficient Framework for Active Finetuning Big transfer (bit): General visual representation learning,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 00b54c58-5688-45c4-b540-aacd8f8717ab · outbound

This paper cites Scaling vision transformers,.

FACT: A Simple and Efficient Framework for Active Finetuning Scaling vision transformers,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 76d2793f-79b8-461c-8d8c-9afc0e829f23 · outbound

This paper cites A progressive batching l-bfgs method for machine learning,.

FACT: A Simple and Efficient Framework for Active Finetuning A progressive batching l-bfgs method for machine learning,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation cd678768-4b2a-4b76-8387-d2ab92d9e1ce · outbound

This paper cites An accelerated linearly convergent stochastic l-bfgs algorithm,.

FACT: A Simple and Efficient Framework for Active Finetuning An accelerated linearly convergent stochastic l-bfgs algorithm,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 32857315-201e-4f1b-bc9a-3ecc07fec5bb · outbound

This paper cites Set trans- former: A framework for attention-based permutation-invariant neural networks,.

FACT: A Simple and Efficient Framework for Active Finetuning Set trans- former: A framework for attention-based permutation-invariant neural networks,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 6344d1d3-48e9-459c-b1c9-62c8ebf6dd1a · outbound

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

FACT: A Simple and Efficient Framework for Active Finetuning Learning multiple layers of features from tiny images,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation fd10b659-a306-457d-a2e4-5db056bbd0bd · outbound

This paper cites Imagenet large scale visual recognition challenge,.

FACT: A Simple and Efficient Framework for Active Finetuning Imagenet large scale visual recognition challenge,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 2b6b7af4-0b13-4324-b7ee-e65d9aa7fffb · outbound

This paper cites Learning imbal- anced datasets with label-distribution-aware margin loss,.

FACT: A Simple and Efficient Framework for Active Finetuning Learning imbal- anced datasets with label-distribution-aware margin loss,

Reference 49

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Observation d4e33a71-5c7f-46d0-ac2e-2186a0b9f5cf · outbound

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

FACT: A Simple and Efficient Framework for Active Finetuning 3d object representations for fine-grained categorization,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation d454a15a-ed30-4cca-8887-cda7a0127d0c · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

FACT: A Simple and Efficient Framework for Active Finetuning Fine-Grained Visual Classification of Aircraft

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-01T22:16:17.180477Z

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Observation a177237c-57ab-4b3e-9fc7-db5a0d1f509f · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

FACT: A Simple and Efficient Framework for Active Finetuning Emerging properties in self-supervised vision transformers,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation a02268c0-17af-409a-afb6-1346fa753543 · outbound

This paper cites MMSegmentation: Openmmlab semantic seg- mentation toolbox and benchmark,.

FACT: A Simple and Efficient Framework for Active Finetuning MMSegmentation: Openmmlab semantic seg- mentation toolbox and benchmark,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation 3757fafb-5299-4914-bf6b-f5fa5eaf5f75 · outbound

This paper cites Unified perceptual parsing for scene understanding,.

FACT: A Simple and Efficient Framework for Active Finetuning Unified perceptual parsing for scene understanding,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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Observation c67f2f14-4227-4661-84eb-208b6be0a5ec · outbound

This paper cites Scene parsing through ade20k dataset,.

FACT: A Simple and Efficient Framework for Active Finetuning Scene parsing through ade20k dataset,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-28T15:28:21.303268Z

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