Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models

• URL: http://arxiv.org/abs/2403.01101v1
• Date: Sat, 2 Mar 2024 06:01:34 GMT
• Title: Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models
• Authors: Ziting Wen, Oscar Pizarro, Stefan Williams
• Abstract summary: Fine-tuning the pre-trained model with active learning holds promise for reducing annotation costs. Recent research has proposed proxy-based active learning, which pre-computes features to reduce computational costs. This approach often incurs a significant loss in active learning performance, which may even outweigh the computational cost savings.
• Score: 5.2976735459795385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning the pre-trained model with active learning holds promise for reducing annotation costs. However, this combination introduces significant computational costs, particularly with the growing scale of pre-trained models. Recent research has proposed proxy-based active learning, which pre-computes features to reduce computational costs. Yet, this approach often incurs a significant loss in active learning performance, which may even outweigh the computational cost savings. In this paper, we argue the performance drop stems not only from pre-computed features' inability to distinguish between categories of labeled samples, resulting in the selection of redundant samples but also from the tendency to compromise valuable pre-trained information when fine-tuning with samples selected through the proxy model. To address this issue, we propose a novel method called aligned selection via proxy to update pre-computed features while selecting a proper training method to inherit valuable pre-training information. Extensive experiments validate that our method significantly improves the total cost of efficient active learning while maintaining computational efficiency.

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