If anyone in the PyTorch community is interested in taking a crack at the NIPS 2017 Adversarial Attack and Defense challenges (https://www.kaggle.com/c/nips-2017-targeted-adversarial-attack 46), I’ve posted PyTorch baseline implementations that work within the competition framework:

  
      
      GitHub
  
  
    

rwightman/pytorch-nips2017-attack-example 171

pytorch-nips2017-attack-example - A PyTorch baseline attack example for the NIPS 2017 adversarial competition


  
  
    
    
  
  



  
      
      GitHub
  
  
    

rwightman/pytorch-nips2017-defense-example 78

pytorch-nips2017-defense-example - A PyTorch baseline defense example for the NIPS 2017 adversarial competition


  
  
    
    
  
  


They currently use the torchvision Inception-v3 model and pretrained weights to be comparable to the baseline Tensorflow models provided. Additionally, as per requirements, they rely on a Docker image that I’ve built from the PyTorch Dockerfile (recently updated to v0.2.0, https://hub.docker.com/r/rwightman/pytorch-extra/ 16)
The more people involved, the more interesting it’ll get…