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Many well-trained Convolutional Neural Network(CNN) models have now beenreleased online by developers for the sake of effortless reproducing. In thispaper, we treat such pre-trained networks as teachers and explore how to learna target student network for customized tasks, using multiple teachers thathandle different tasks. We assume no human-labelled annotations are available,and each teacher model can be either single- or multi-task network, where theformer is a degenerated case of the latter. The student model, depending on thecustomized tasks, learns the related knowledge filtered from the multipleteachers, and eventually masters the complete or a subset of expertise from allteachers. To this end, we adopt a layer-wise training strategy, which entanglesthe student's network block to be learned with the corresponding teachers. Asdemonstrated on several benchmarks, the learned student network achieves verypromising results, even outperforming the teachers on the customized tasks.

Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers