Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions

• URL: http://arxiv.org/abs/2411.10364v1
• Date: Fri, 15 Nov 2024 17:14:18 GMT
• Title: Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions
• Authors: Tianhao Ma, Han Chen, Juncheng Hu, Yungang Zhu, Ximing Li,
• Abstract summary: Learning from label proportions (LLP) aims to train a classifier by using bags of instances and the proportions of classes within bags, rather than annotated labels for each instance. We propose a novel LLP method, namely Learning from Label Proportions with Auxiliary High-confident Instance-level Loss (L2P-AHIL) We show that L2P-AHIL can surpass the existing baseline methods, and the performance gain can be more significant as the bag size increases.
• Score: 17.36538357653019
• License:
• Abstract: Learning from label proportions (LLP), i.e., a challenging weakly-supervised learning task, aims to train a classifier by using bags of instances and the proportions of classes within bags, rather than annotated labels for each instance. Beyond the traditional bag-level loss, the mainstream methodology of LLP is to incorporate an auxiliary instance-level loss with pseudo-labels formed by predictions. Unfortunately, we empirically observed that the pseudo-labels are are often inaccurate due to over-smoothing, especially for the scenarios with large bag sizes, hurting the classifier induction. To alleviate this problem, we suggest a novel LLP method, namely Learning from Label Proportions with Auxiliary High-confident Instance-level Loss (L^2P-AHIL). Specifically, we propose a dual entropy-based weight (DEW) method to adaptively measure the confidences of pseudo-labels. It simultaneously emphasizes accurate predictions at the bag level and avoids overly smoothed predictions. We then form high-confident instance-level loss with DEW, and jointly optimize it with the bag-level loss in a self-training manner. The experimental results on benchmark datasets show that L^2P-AHIL can surpass the existing baseline methods, and the performance gain can be more significant as the bag size increases.

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