A Variational Information Bottleneck Based Method to Compress Sequential Networks for Human Action Recognition

@article{Srivastava2021AVI,
  title={A Variational Information Bottleneck Based Method to Compress Sequential Networks for Human Action Recognition},
  author={Ayush Srivastava and Oshin Dutta and A. P. Prathosh and Sumeet Agarwal and Jigyasa Gupta},
  journal={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2021},
  pages={2744-2753}
}
In the last few years, deep neural networks’ compression has become an important strand of machine learning and computer vision research. Deep models require sizeable computational complexity and storage when used, for instance, for Human Action Recognition (HAR) from videos, making them unsuitable to be deployed on edge devices. In this paper, we address this issue and propose a method to effectively compress Recurrent Neural Networks (RNNs) such as Gated Recurrent Units (GRUs) and Long-Short… 

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