Gradient Forward-Propagation for Large-Scale Temporal Video Modelling

@article{Malinowski2021GradientFF,
  title={Gradient Forward-Propagation for Large-Scale Temporal Video Modelling},
  author={Mateusz Malinowski and Dimitrios Vytiniotis and Grzegorz Swirszcz and Viorica Patraucean and Jo{\~a}o F. M. Carreira},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={9245-9255}
}
How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the forward and backward passes are completed. For temporal signals, this introduces high latency and hinders real-time learning. It also creates a coupling between consecutive layers, which limits model parallelism and increases memory consumption. In this paper, we build upon Sideways, which avoids blocking by… 

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