Deep Partial Multi-Label Learning with Graph Disambiguation

• URL: http://arxiv.org/abs/2305.05882v1
• Date: Wed, 10 May 2023 04:02:08 GMT
• Title: Deep Partial Multi-Label Learning with Graph Disambiguation
• Authors: Haobo Wang, Shisong Yang, Gengyu Lyu, Weiwei Liu, Tianlei Hu, Ke Chen, Songhe Feng, Gang Chen
• Abstract summary: We propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN) Specifically, we introduce the instance-level and label-level similarities to recover label confidences. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels.
• Score: 27.908565535292723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevalent to deal with PML problems. However, we observe that existing graph-based PML methods typically adopt linear multi-label classifiers and thus fail to achieve superior performance. In this work, we attempt to remove several obstacles for extending them to deep models and propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN). Specifically, we introduce the instance-level and label-level similarities to recover label confidences as well as exploit label dependencies. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels; then, we train the deep model to fit the numerical labels. Moreover, we provide a careful analysis of the risk functions to guarantee the robustness of the proposed model. Extensive experiments on various synthetic datasets and three real-world PML datasets demonstrate that PLAIN achieves significantly superior results to state-of-the-art methods.

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