Semi-supervised Tuning from Temporal Coherence
This work addresses incremental tuning for semi-supervised learning in computer vision, offering a domain-specific improvement.
The paper tackles the problem of improving classification accuracy in semi-supervised learning by leveraging temporal coherence from unlabeled video sequences, showing that a mildly supervised deep architecture can progressively enhance its performance, sometimes approaching supervised levels.
Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures. In particular, enforcing smooth output changes while presenting temporally-closed frames from video sequences, proved to be an effective strategy. In this paper we prove the efficacy of temporal coherence for semi-supervised incremental tuning. We show that a deep architecture, just mildly trained in a supervised manner, can progressively improve its classification accuracy, if exposed to video sequences of unlabeled data. The extent to which, in some cases, a semi-supervised tuning allows to improve classification accuracy (approaching the supervised one) is somewhat surprising. A number of control experiments pointed out the fundamental role of temporal coherence.