不用人工规则，让多个教师模型自动协作提升效果。
Heuristic-Free Multi-Teacher Learning
- 用教师专属输入标记，把多教师预测转为多个辅助任务
- 在多个数据集和模型上表现优于传统方法
- 适合需要多专家协同的场景，如医学图像分析
我们提出Teacher2Task框架，解决多教师学习中依赖人工聚合规则的问题。传统方法常因聚合规则不当导致标签质量下降并传播误差。该框架将包含真实标签和N个教师标注的数据，转化为N+1个任务：其中N个辅助任务用于预测每个教师的标注风格，主任务则专注于真实标签。该方法融合多种学习范式，在多种架构、模态和任务上均取得显著效果。
原文摘要 · Abstract (English)
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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