Authors: Huy Thong Nguyen, En-Hung Chu, Lenord Melvix, Jazon Jiao, Chunglin Wen, Benjamin Louie
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.
Source: http://arxiv.org/abs/2411.12724v1
