首页|Sequential Cooperative Distillation for Imbalanced Multi-Task Learning
Sequential Cooperative Distillation for Imbalanced Multi-Task Learning
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万方数据
维普
Multi-task learning(MTL)can boost the performance of individual tasks by mutual learning among multi-ple related tasks.However,when these tasks assume diverse complexities,their corresponding losses involved in the MTL objective inevitably compete with each other and ultimately make the learning biased towards simple tasks rather than complex ones.To address this imbalanced learning problem,we propose a novel MTL method that can equip multiple ex-isting deep MTL model architectures with a sequential cooperative distillation(SCD)module.Specifically,we first intro-duce an efficient mechanism to measure the similarity between tasks,and group similar tasks into the same block to allow their cooperative learning from each other.Based on this,the grouped task blocks are sorted in a queue to determine the learning sequence of the tasks according to their complexities estimated with the defined performance indicator.Finally,a distillation between the individual task-specific models and the MTL model is performed block by block from complex to simple manner,achieving a balance between competition and cooperation among learning multiple tasks.Extensive experi-ments demonstrate that our method is significantly more competitive compared with state-of-the-art methods,ranking No.1 with average performances across multiple datasets by improving 12.95％and 3.72％compared with OMTL and MTLKD,respectively.
万方数据
维普
