Multi-task classification with sequential instances and tasks

Wei Xu, Wei Liu, Haoyuan Chi, Xiaolin Huang, Jie Yang

Abstract: Highlights •We propose a new multi-task classification framework MTSIT that considers both the complexity of tasks and that of instances.•We provide a theoretical explanation for MTSIT, which is the first to incorporate SPL into the multi-task classification based on the PAC-Bayesian theory.•Experimental results on three real-world computer vision datasets demonstrate the effectiveness of the proposed approach.