Heuristic-Free Multi-Teacher Learning

• URL: http://arxiv.org/abs/2411.12724v1
• Date: Tue, 19 Nov 2024 18:45:16 GMT
• Title: Heuristic-Free Multi-Teacher Learning
• Authors: Huy Thong Nguyen, En-Hung Chu, Lenord Melvix, Jazon Jiao, Chunglin Wen, Benjamin Louie,
• Abstract summary: Teacher2Task is a novel framework for multi-teacher learning that eliminates the need for manual aggregations. Instead of relying on aggregated labels, the framework transforms the training data, consisting of ground truth labels and annotations from N teachers, into N+1 distinct tasks.
• Score: 0.6597195879147557
• License:
• Abstract: We introduce Teacher2Task, a novel framework for multi-teacher learning that eliminates the need for manual aggregation heuristics. Existing multi-teacher methods typically rely on such heuristics to combine predictions from multiple teachers, often resulting in sub-optimal aggregated labels and the propagation of aggregation errors. Teacher2Task addresses these limitations by introducing teacher-specific input tokens and reformulating the training process. Instead of relying on aggregated labels, the framework transforms the training data, consisting of ground truth labels and annotations from N teachers, into N+1 distinct tasks: N auxiliary tasks that predict the labeling styles of the N individual teachers, and one primary task that focuses on the ground truth labels. This approach, drawing upon principles from multiple learning paradigms, demonstrates strong empirical results across a range of architectures, modalities, and tasks.

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