Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning

Uta Büchler; Biagio Brattoli; Björn Ommer

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arXiv · 1807.11293

Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning

Uta Büchler·Biagio Brattoli·Björn Ommer

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Abstract

Self-supervised learning of convolutional neural networks can harness large amounts of cheap unlabeled data to train powerful feature representations. As surrogate task, we jointly address ordering of visual data in the spatial and temporal domain. The permutations of training samples, which are at the core of self-supervision by ordering, have so far been sampled randomly from a fixed preselected set. Based on deep reinforcement learning we propose a sampling policy that adapts to the state of the network, which is being trained. Therefore, new permutations are sampled according to their expected utility for updating the convolutional feature representation. Experimental evaluation on unsupervised and transfer learning tasks demonstrates competitive performance on standard benchmarks for image and video classification and nearest neighbor retrieval.

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BibTeX RIS

Uta Büchler, Biagio Brattoli, Björn Ommer. 2018-07-30. Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning. https://arxiv.org/abs/1807.11293

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