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We propose a memory efficient method, named Stochastic Backpropagation (SBP),for training deep neural networks on videos. It is based on the finding thatgradients from incomplete execution for backpropagation can still effectivelytrain the models with minimal accuracy loss, which attributes to the highredundancy of video. SBP keeps all forward paths but randomly and independentlyremoves the backward paths for each network layer in each training step. Itreduces the GPU memory cost by eliminating the need to cache activation valuescorresponding to the dropped backward paths, whose amount can be controlled byan adjustable keep-ratio. Experiments show that SBP can be applied to a widerange of models for video tasks, leading to up to 80.0% GPU memory saving and10% training speedup with less than 1% accuracy drop on action recognition andtemporal action detection.

Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models