Parameter-Efficient Active Learning for Foundational models
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Abstract (arXiv)
Foundational vision transformer models have shown impressive few shot performance on many vision tasks. This research presents a novel investigation into the application of parameter efficient fine-tuning methods within an active learning (AL) framework, to advance the sampling selection process in extremely budget constrained classification tasks. The focus on image datasets, known for their out-of-distribution characteristics, adds a layer of complexity and relevance to our study. Through a detailed evaluation, we illustrate the improved AL performance on these challenging datasets, highlighting the strategic advantage of merging parameter efficient fine tuning methods with foundation models. This contributes to the broader discourse on optimizing AL strategies, presenting a promising avenue for future exploration in leveraging foundation models for efficient and effective data annotation in specialized domains.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 13 Jun 2024.
Builds on
17 of 24 references.
References · 17
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5 more
- 2024Revisiting Active Learning in the Era of Vision Foundation Modelscited by 14
- 2024Querying Easily Flip-flopped Samples for Deep Active Learningcited by 7
- 2023Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settingscited by 5
- 2024STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Modelscited by 5
- 2021Robust Contrastive Active Learning with Feature-guided Query Strategiescited by 2
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