PixMix

Introduction

In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy. These other goals include out-of-distribution (OOD) robustness, prediction consistency, resilience to adversaries, calibrated uncertainty estimates, and the ability to detect anomalous inputs. However, improving performance towards these goals is often a balancing act that today’s methods cannot achieve without sacrificing performance on other safety axes. For instance, adversarial training improves adversarial robustness but sharply degrades other classifier performance metrics. Similarly, strong data augmentation and regularization techniques often improve OOD robustness but harm anomaly detection, raising the question of whether a Pareto improvement on all existing safety measures is possible. To meet this challenge, we design a new data augmentation strategy utilizing the natural structural complexity of pictures such as fractals, which outperforms numerous baselines, is near Pareto-optimal, and comprehensively improves safety measures.

Read the paper here.

Pseudocode

Contents

pixmix_utils.py includes reference implementation of augmentations and mixings used in PixMix.

We also include PyTorch implementations of PixMix on both CIFAR-10/100 and ImageNet in cifar.py and imagenet.py respectively, which both support training and evaluation on CIFAR-10/100-C and ImageNet-C/R.

Usage

Training recipes used in our paper:

CIFAR:

python cifar.py \
  --dataset 
   
     \
  --data-path 
    
      \
  --mixing-set 
     
       \
  --all-ops

     
    
   

ImageNet 1K:

python imagenet.py \
  --data-standard 
   
     \
  --data-val 
    
      \
  --imagenet-r-dir 
     
       \
  --imagenet-c-dir 
      
        \
  --mixing-set 
       
         \ --num-classes 1000 \ --all-ops 
       
      
     
    
   

Mixing Set

The mixing set of fractals and feature visualizations used in the paper can be downloaded here.

Pretrained Models

Weights for a 40x4-WRN CIFAR-10/100 classifier trained with PixMix for 100 epochs are available here.

Weights for a ResNet-50 ImageNet classifier trained with PixMix for 90 and 180 epochs are available here.

Citation

If you find this useful in your research, please consider citing:

@article{hendrycks2022robustness,
  title={PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures},
  author={Dan Hendrycks and Andy Zou and Mantas Mazeika and Leonard Tang and Dawn Song and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2112.05135},
  year={2022}
}