EfficientNet Annotated Paper

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

This paper created a huge mark in the field of model scaling and parametric optimization in terms of model architecture. It brings us a new scaling method called compund scaling, to scale the convolution network in all the three dimensions - Width, Depth and, resolution/channels. Along with this novel way of scaling it also brings us a new family of architecture created using Neural Architecture Search called the EfficentNet Family.

Please feel free to read along with the paper with my notes and highlights.

@misc{tan2020efficientnet,
      title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, 
      author={Mingxing Tan and Quoc V. Le},
      year={2020},
      eprint={1905.11946},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}