Merge pull request #51 from Immortalise/main

add "Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning" ICCV 2023

README.md

@@ -21,8 +21,12 @@
 Latest research in robust machine learning, including adversarial/backdoor attack and defense, out-of-distribution (OOD) generalization, and safe transfer learning.
 
 Hosted projects:
+
+- **RiFT** (ICCV 2023, #Adversarial Robustness, #Generalization, #OOD)
+  - [Code](./RiFT/) | [Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning](https://arxiv.org/abs/2308.02533)
+
 - **Diversify** (ICLR 2023, #OOD):
-  - [Code](./diversify/) | [Out-of-distribution Representation Learning for Time Series Classification](https://arxiv.org/abs/2209.07027)
+  - [Code](./diversify/) | [Out-of-distribution Representatio[n Learning for Time Series Classification](https://arxiv.org/abs/2209.07027)
 - **DRM** (KDD 2023, #OOD):
   - [Code](./drm/) | [Domain-Specific Risk Minimization for Out-of-Distribution Generalization](https://arxiv.org/abs/2208.08661)
 - **DDLearn** (KDD 2023, #OOD):

RiFT/.gitignore

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+data/
+results*/
+
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RiFT/README.md

@@ -0,0 +1,105 @@
+![](https://files.mdnice.com/user/45288/023bf2cb-1685-43ce-bba8-1ba9b66f80b4.png)
+
+# Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning
+
+This is the official implementation of ICCV2023 [Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning](https://arxiv.org/abs/2308.02533).
+
+**Abstract**: Deep neural networks are susceptible to adversarial examples, posing a significant security risk in critical applications. Adversarial Training (AT) is a well-established technique to enhance adversarial robustness, but it often comes at the cost of decreased generalization ability. This paper proposes Robustness Critical Fine-Tuning (RiFT), a novel approach to enhance generalization without compromising adversarial robustness. The core idea of RiFT is to exploit the redundant capacity for robustness by fine-tuning the adversarially trained model on its non-robust-critical module. To do so, we introduce module robust criticality (MRC), a measure that evaluates the significance of a given module to model robustness under worst-case weight perturbations. Using this measure, we identify the module with the lowest MRC value as the non-robust-critical module and fine-tune its weights to obtain fine-tuned weights. Subsequently, we linearly interpolate between the adversarially trained weights and fine-tuned weights to derive the optimal fine-tuned model weights. We demonstrate the efficacy of RiFT on ResNet18, ResNet34, and WideResNet34-10 models trained on CIFAR10, CIFAR100, and Tiny-ImageNet datasets. Our experiments show that RiFT can significantly improve both generalization and out-of-distribution robust- ness by around 1.5% while maintaining or even slightly enhancing adversarial robustness. Code is available at https://github.com/microsoft/robustlearn.
+
+## Requirements
+
+### Running Enviroments
+
+To install requirements:
+
+```
+conda env create -f env.yaml
+conda activate rift
+```
+
+### Datasets
+
+CIFAR10 and CIFAR100 can be downloaded via PyTorch.
+
+For other datasets:
+
+1. [Tiny-ImageNet](http://cs231n.stanford.edu/tiny-imagenet-200.zip)
+2. [CIFAR10-C](https://drive.google.com/drive/folders/1HDVw6CmX3HiG0ODFtI75iIfBDxSiSz2K)
+3. [CIFAR100-C](https://drive.google.com/drive/folders/1HDVw6CmX3HiG0ODFtI75iIfBDxSiSz2K)
+4. [Tiny-ImageNet-C](https://berkeley.app.box.com/s/6zt1qzwm34hgdzcvi45svsb10zspop8a)
+
+After downloading these datasets, move them to ./data. 
+
+The images in Tiny-ImageNet datasets are 64x64 with 200 classes.
+
+## Robust Critical Fine-Tuning
+
+### Demo
+
+Here we present a example for RiFT ResNet18 on CIFAR10.
+
+Download the adversarially trained model weights [here](https://drive.google.com/drive/folders/1Uzqm1cOYFXLa97GZjjwfiVS2OcbpJK4o?usp=drive_link).
+
+```
+python main.py --layer=layer2.1.conv2 --resume="./ResNet18_CIFAR10.pth"
+```
+
+- layer: the desired layer name to fine-tune.
+
+Here, layer2.1.conv2 is a non-robust-critical module.
+
+The non-robust-critical module of each model on each dataset are summarized as follows:
+
+|          | CIFAR10              | CIFAR100             | Tiny-ImageNet        |
+| -------- | -------------------- | -------------------- | -------------------- |
+| ResNet18 | layer2.1.conv2       | layer2.1.conv2       | layer3.1.conv2       |
+| ResNet34 | layer2.3.conv2       | layer2.3.conv2       | layer3.5.conv2       |
+| WRN34-10 | block1.layer.3.conv2 | block1.layer.2.conv2 | block1.layer.2.conv2 |
+
+### Pipeline
+
+1. Characterize the MRC for each module
+   `python main.py --cal_mrc --resume=/path/to/your/model`
+   This will output  the MRC for each module.
+2. Fine-tuning on non-robust-critical module
+   Based on the MRC output, choose a module with lowest MRC value to fine-tune. 
+   We suggest to choose the **middle layers** according to our experience.
+   Try different learning rate! Usually a small learning rate is preferred.
+   `python main.py --layer=xxx --lr=yyy  --resume=zzz`
+   When fine-tuning finish, it will automatically interpolate between adversarially trained weights and fine-tuned weights.
+   The robust accuracy, in-distribution test acc are evaluated during the interpolation procedure.
+3. Test OOD performance. Pick he best interpolation factor (the one with max IID generalization increase while not drop robustness so much.)
+   `python eval_ood.py --resume=xxx`
+
+## Results
+
+![](https://files.mdnice.com/user/45288/c3c98491-a292-4888-82cc-081bc8d3c3c6.png)
+
+
+
+
+![](https://files.mdnice.com/user/45288/bad5bb9f-788d-4350-ac5c-ddd850ade04f.png)
+
+
+
+## References & Opensources
+
+- Classification models [code](https://github.com/kuangliu/pytorch-cifar)
+- Adversarial training [code](https://github.com/P2333/Bag-of-Tricks-for-AT)
+
+## Contact
+
+- Kaijie Zhu: [email protected]
+- Jindong Wang: [email protected]
+
+## Citation
+
+```
+@inproceedings{zhu2023improving,
+	title={Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning}, 
+	author={Zhu, Kaijie and Hu, Xixu and Wang, Jindong and Xie, Xing and Yang, Ge },
+	year={2023},
+	booktitle={International Conference on Computer Vision},
+}
+```
+