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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

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# 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).|main hypothesis of the paper
|:--|---|

HFC helps explain CNN generaliation

**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)