Do Better ImageNet Models Transfer Better? Paper Summary & Analysis

Paper Objective

Background

  1. Fixed feature extraction: the final layer of the Image-Net trained network is removed in favour of a linear classifier, which outputs the class prediction over the classes of the new (target) dataset.
  2. Fine-tuning: the weights of the ImageNet pretrained model are treated as an initialisation for the model trained on the new (target) dataset

Paper Contributions

  1. the absence of a scale parameter (γ) for batch normalization layers
  2. the use of label smoothing
  3. dropout
  4. the presence of an auxiliary classifier head
https://openaccess.thecvf.com/content_CVPR_2019/papers/Kornblith_Do_Better_ImageNet_Models_Transfer_Better_CVPR_2019_paper.pdf

Conclusion

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Cornell Data Science

Cornell Data Science

Cornell Data Science is an engineering project team @Cornell that seeks to prepare students for a career in data science.

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