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.

本研究针对现有多教师学习方法依赖手动聚合启发式而导致的聚合标签亚优化和错误传播问题，提出了一种新颖的框架Teacher2Task。该框架通过引入教师特定的输入标记和重新构建训练过程，将训练数据转化为N+1个不同的任务，展示了在多种架构、模态和任务上的强大实证结果。

无启发式多教师学习