This repo contains the implementation of experiments in the paper
A Closer Look at Accuracy vs. Robustness
Authors: Yao-Yuan Yang*, Cyrus Rashtchian*, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri (* equal contribution)
Appeared in NeurIPS 2020 (link)
Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning. We take a closer look at this phenomenon and first show that real image datasets are actually separated. With this property in mind, we then prove that robustness and accuracy should both be achievable for benchmark datasets through locally Lipschitz functions, and hence, there should be no inherent tradeoff between robustness and accuracy. Through extensive experiments with robustness methods, we argue that the gap between theory and practice arises from two limitations of current methods: either they fail to impose local Lipschitzness or they are insufficiently generalized. We explore combining dropout with robust training methods and obtain better generalization. We conclude that achieving robustness and accuracy in practice may require using methods that impose local Lipschitzness and augmenting them with deep learning generalization techniques.
pip install -r ./requirements.txt
pip install --upgrade git+https://github.com/tensorflow/cleverhans.git#egg=cleverhans
Use the script ./scripts/restrictedImgNet.py to generate restrictedImgNet
dataset and put the data in ./data/RestrictedImgNet/ with torchvision
ImageFolder readable format. For more detail, please refer to
lolip/dataset/__init__.py.
-
The spline example notebooks/splines.ipynb
-
The code for getting train-train separation and test-train separation notebooks/dataset_dist.ipynb
-
The script to run proof-of-concept classifiers scripts/run_proof_of_concept.sh
-
The script to run separation for Restricted ImageNet scripts/restrictedImgNet_dists.py
-
The script to run separation for randomly labeled Restricted ImageNet scripts/random_restrictedImgNet_dists.py
The default training parameters are set in lolip/models/__init__.py
The network architectures defined in lolip/models/torch_utils/archs.py
arch: ("CNN001", "CNN002", "WRN_40_10", "WRN_40_10_drop20", "WRN_40_10_drop50", "ResNet50", "ResNet50_drop50")
- Natural: ce-tor-{arch}
- TRADES(beta=6): strades6ce-tor-{arch}
- adversarial training: advce-tor-{arch}
- RST(lambda=2): advbeta2ce-tor-{arch}
- TULIP(gradient regularization): tulipce-tor-{arch}
- LLR: sllrce-tor-{arch}
Run Natural training with CNN001 on the MNIST dataset
Perturbation distance is set to
python ./main.py --experiment experiment01 \
--no-hooks \
--norm inf --eps 0.1 \
--dataset mnist \
--model ce-tor-CNN001 \
--attack pgd \
--random_seed 0
Run TRADES (beta=6) with Wide ResNet 40-10 on the Cifar10 dataset
Perturbation distance is set to 0.031 with L infinity norm.
Batch size is
python ./main.py --experiment experiment01 \
--no-hooks \
--norm inf --eps 0.031 \
--dataset cifar10 \
--model strades6ce-tor-WRN_40_10 \
--attack pgd \
--random_seed 0
Run adversarial training with ResNet50 on the Restricted ImageNet dataset.
Perturbation distance is set to 0.005 with L infinity norm.
Attack with PGD attack.
Batch size is
python ./main.py --experiment restrictedImgnet \
--no-hooks \
--norm inf --eps 0.005 \
--dataset resImgnet112v3 \
--model advce-tor-ResNet50-adambs128 \
--attack pgd \
--random_seed 0
- Table1 and Figures in Appendix D: notebooks/dataset_dist.ipynb
- Table2: scripts/Table2.sh
- Table3: scripts/Table3.sh
- Table4: scripts/Table4.sh
Run Robust self training (lambda=2) with Wide ResNet 40-10 on the Cifar10 dataset
Perturbation distance is set to 0.031 with L infinity norm.
Batch size is
python ./main.py --experiment hypo \
--no-hooks \
--norm inf --eps 0.031 \
--dataset cifar10 \
--model advbeta2ce-tor-WRN_40_10 \
--attack pgd \
--random_seed 0