Shape or Texture: Understanding Discriminative Features in CNNs
Md Amirul Islam, Matthew Kowal, Patrick Esser, Sen Jia, Björn Ommer, Konstantinos G. Derpanis, Neil Bruce

Stylized PASCAL VOC 2012 Preparation

1. Download the PASCAL VOC 2012 dataset from official website.

2. Run the following script to generate the stylized voc 2012 dataset

        cd generate_stylized_voc
        python make_stl_voc.py
   

1. Change --data_path in config to point to the stylized VOC dataset location

2. To estimate the shape and texture neurons in a ResNet50 and ViT-16-Base-224 using SVOC (args and results saved to --save_dir), run:

        python main.py --model resnet50 --image_size 513 --save_dir dim_outputs/svoc/resnet50
        python main.py --model vit_base_patch16_224  --image_size 224 --save_dir dim_outputs/svoc/ViT-16-Base-224
   

Note that the results are in the format [SHAPE, TEXTURE, RESIDUAL] and display both the number of neurons and percentages.

For more examples of run configurations, see the files 'run_CNNs_dim_estimation.sh' and 'run_transformer_dim_estimation.sh'

If you want to use your own model, you need it to output a global-pooled latent representation. For an example, see the '_forward_impl' function in models.resnet.py. The models in the 'models' directory are changed in this way already. You should also load the checkpoint in utils.py when the model is selected.

If you find this repository useful, please consider giving a star ⭐ and citation 🦖

  @InProceedings{islam2021shape,
   title={Shape or texture: Understanding discriminative features in CNNs},
   author={Islam, Md Amirul and Kowal, Matthew and Esser, Patrick and Jia, Sen and Ommer, Bjorn and Derpanis, Konstantinos G and Bruce, Neil},
   booktitle={International Conference on Learning Representations},
   year={2021}
 }