Towards Robust Incremental Learning under Ambiguous Supervision

• URL: http://arxiv.org/abs/2501.13584v3
• Date: Tue, 18 Feb 2025 02:37:12 GMT
• Title: Towards Robust Incremental Learning under Ambiguous Supervision
• Authors: Rui Wang, Mingxuan Xia, Chang Yao, Lei Feng, Junbo Zhao, Gang Chen, Haobo Wang,
• Abstract summary: We propose a novel weakly-supervised learning paradigm called Incremental Partial Label Learning (IPLL) IPLL aims to handle sequential fully-supervised learning problems where novel classes emerge from time to time. We develop a memory replay technique that collects well-disambiguated samples while maintaining representativeness and diversity.
• Score: 22.9111210739047
• License:
• Abstract: Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotation uncertainty and ambiguity, collecting high-quality annotated data in a dynamic learning system can be extremely expensive. To mitigate this problem, we propose a novel weakly-supervised learning paradigm called Incremental Partial Label Learning (IPLL), where the sequentially arrived data relate to a set of candidate labels rather than the ground truth. Technically, we develop the Prototype-Guided Disambiguation and Replay Algorithm (PGDR) which leverages the class prototypes as a proxy to mitigate two intertwined challenges in IPLL, i.e., label ambiguity and catastrophic forgetting. To handle the former, PGDR encapsulates a momentum-based pseudo-labeling algorithm along with prototype-guided initialization, resulting in a balanced perception of classes. To alleviate forgetting, we develop a memory replay technique that collects well-disambiguated samples while maintaining representativeness and diversity. By jointly distilling knowledge from curated memory data, our framework exhibits a great disambiguation ability for samples of new tasks and achieves less forgetting of knowledge. Extensive experiments demonstrate that PGDR achieves superior

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