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Learning in the Frequency Domain

Highlights

  • We propose a method of learning in the frequency domain (using DCT coefficients as input), which requires little modification to the existing CNN models that take RGB input.
  • We show that learning in the frequency domain better preserves image information in the pre-processing stage than the conventional spatial downsampling approach.
  • We propose a learning-based dynamic channel selection method to identify the trivial frequency components for static removal during inference. Experiment results on ResNet-50 show that one can prune up to $87.5%$ of the frequency channels using the proposed channel selection method with no or little accuracy degradation in the ImageNet classification task.
  • To the best of our knowledge, this is the first work that explores learning in the frequency domain for high-level vision tasks, such as object detection and instance segmentation.

Please refer to the image classfication and instance segmentation sections for more details.

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  • Python 88.8%
  • Cuda 7.3%
  • C++ 3.7%
  • Other 0.2%