The article introduces the Bi-Level Active Finetuning Framework (BiLAF) to address challenges in active learning methods for sample selection. It focuses on balancing diversity and uncertainty by selecting central samples for diversity and boundary samples for uncertainty. The framework operates in two stages: Core Samples Selection and Boundary Samples Selection. The process starts with identifying pseudo-class centers, followed by denoising methods and iterative strategies for boundary sample selection without relying on ground-truth labels. Extensive experiments demonstrate the efficacy of the method, outperforming existing baselines significantly. The article also discusses related work, decision boundaries in neural networks, and the importance of boundary samples in classification tasks.
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by Han Lu,Yiche... lúc arxiv.org 03-18-2024
https://arxiv.org/pdf/2403.10069.pdfYêu cầu sâu hơn