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The instance discrimination paradigm has become dominant in unsupervisedlearning. It always adopts a teacher-student framework, in which the teacherprovides embedded knowledge as a supervision signal for the student. Thestudent learns meaningful representations by enforcing instance spatialconsistency with the views from the teacher. However, the outputs of theteacher can vary dramatically on the same instance during different trainingstages, introducing unexpected noise and leading to catastrophic forgettingcaused by inconsistent objectives. In this paper, we first integrate instancetemporal consistency into current instance discrimination paradigms, andpropose a novel and strong algorithm named Temporal Knowledge Consistency(TKC). Specifically, our TKC dynamically ensembles the knowledge of temporalteachers and adaptively selects useful information according to its importanceto learning instance temporal consistency. Experimental result shows that TKCcan learn better visual representations on both ResNet and AlexNet on linearevaluation protocol while transfer well to downstream tasks. All experimentssuggest the good effectiveness and generalization of our method.

Temporal Knowledge Consistency for Unsupervised Visual Representation Learning