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.

Color	Meaning
Green	Topics about the current paper
Yellow	Topics about other relevant references
Blue	Implementation details/ maths
Red	Text including my thoughts, questions, and understandings

@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}
}