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https://github.com/HaohanWang/HFC

Implementation for the paper (CVPR Oral): High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
https://github.com/HaohanWang/HFC

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Implementation for the paper (CVPR Oral): High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

Host: GitHub
URL: https://github.com/HaohanWang/HFC
Owner: HaohanWang
Created: 2020-03-24T04:16:46.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2021-03-24T20:27:09.000Z (over 3 years ago)
Last Synced: 2024-01-16T10:46:07.831Z (5 months ago)
Language: Python
Homepage: https://arxiv.org/abs/1905.13545
Size: 22.6 MB
Stars: 229
Watchers: 4
Forks: 29
Open Issues: 1
Metadata Files:
- Readme: README.md

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awesome-stars - HaohanWang/HFC - Implementation for the paper (CVPR Oral): High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks (Python)

README

        # High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks 

**[H. Wang, X. Wu, Z. Huang, and E. P. Xing. "High frequency component helps explain the generalization of convolutional neural networks." CVPR 2020 (Oral).](https://arxiv.org/abs/1905.13545)**

**[\[Slides\]](https://drive.google.com/file/d/1fetghgWA91seJHvBmeVQI5AaB-yyfbwN/view?usp=sharing)** and 

**[\[Poster\]](https://github.com/HaohanWang/HFC/blob/master/poster.png)**

## Highlights

|Fig. 1: The central hypothesis of our paper: within a data collection, there are correlations between the highfrequency components and the “semantic” component of the images. As a result, the model will perceive both high-frequency components as well as the “semantic” ones, leading to generalization behaviors counterintuitive to human (e.g., adversarial examples).|  

 |:--|---|



**Fig. 2: Eight testing samples selected from CIFAR10 that help explain that CNN can capture the high-frequency image: the model (ResNet18) correctly predicts the original image (1st column in each panel) and the high frequency reconstructed image (3rd column in each panel), but incorrectly predict the low-frequency reconstructed image (2nd column in each panel). The prediction confidences are also shown. Details are in the paper.**

 Other Discussions in Paper (click to expand)

  1. Trade-off between accuracy and robustness (Section 3)

  2. Rethinking data before rethinking generalization (Section 4)

  3. Re-evaluate the heuristics (BatchNorm seems to promote high-frequency information) (Section 5)

  4. Adversarially robust models tend to filter out high-frequency components (Section 6)

  5. Similar phenomena are observed beyond image classification (Section 7)

## Code Structures

- [scripts/resnet.py](https://github.com/HaohanWang/HFC/blob/master/scripts/resnet.py)

    - Pipeline to replicate our results on CIFAR10 data

- utility/ 

    - [attackHelper.py](https://github.com/HaohanWang/HFC/blob/master/utility/attackHelper.py)

         - main methods used to generate adversarial examples

    - [dataLoader.py](https://github.com/HaohanWang/HFC/blob/master/utility/dataLoader.py)

         - load data

    - [frequencyHelper.py](https://github.com/HaohanWang/HFC/blob/master/utility/frequencyHelper.py)

         - generate low and high frequency data

    - [pdg_attack.py](https://github.com/HaohanWang/HFC/blob/master/utility/pgd_attack.py)

         - helper for adversarial training

## Before using the code

- Install the main dependency: TensorFlow 1.x and [Foolbox](https://github.com/bethgelab/foolbox)

- Read the main script. We recommend users to skim through the script befoure usage. We use "todo" to highlight the parts that may require attention. 

- Generate data: one can generate the data with [utility/frequencyHelper.py](https://github.com/HaohanWang/HFC/blob/master/utility/frequencyHelper.py)

## PyTorch Implementation

The results were generated by the TensorFlow code as shared here, but for the friends who prefer PyTorch, Xindi has nicely created some codes to help you start: [PyTorch Implementation](https://github.com/HaohanWang/HFC/blob/master/pytorch_implementation.zip)

   

## Contact 

  

[HaohanWang](https://twitter.com/HaohanWang) 

· 

[Xindi Wu](https://github.com/XindiWu) 

·

[Zeyi Huang](https://github.com/Justinhzy)