Sideways: Depth-Parallel Training of Video Models

• URL: http://arxiv.org/abs/2001.06232v3
• Date: Mon, 30 Mar 2020 22:48:10 GMT
• Title: Sideways: Depth-Parallel Training of Video Models
• Authors: Mateusz Malinowski and Grzegorz Swirszcz and Joao Carreira and Viorica Patraucean
• Abstract summary: Sideways is an approximate backpropagation scheme for training video models. We show that Sideways can potentially exhibit better generalization compared to standard synchronized backpropagation.
• Score: 19.370765021278004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Sideways, an approximate backpropagation scheme for training video models. In standard backpropagation, the gradients and activations at every computation step through the model are temporally synchronized. The forward activations need to be stored until the backward pass is executed, preventing inter-layer (depth) parallelization. However, can we leverage smooth, redundant input streams such as videos to develop a more efficient training scheme? Here, we explore an alternative to backpropagation; we overwrite network activations whenever new ones, i.e., from new frames, become available. Such a more gradual accumulation of information from both passes breaks the precise correspondence between gradients and activations, leading to theoretically more noisy weight updates. Counter-intuitively, we show that Sideways training of deep convolutional video networks not only still converges, but can also potentially exhibit better generalization compared to standard synchronized backpropagation.

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