readme.md

Visualizing the Emergence of Intermediate Visual Patterns in DNNs

PyTorch implementation for the paper "Visualizing the Emergence of Intermediate Visual Patterns in DNNs" (NeurIPS 2021)

Requirements

Mainly based on PyTorch and NumPy.

Minimal Examples

To train the sample embeddings $g=Mf$,

from embeddings import SampleEmbedding

sample_emb = SampleEmbedding(in_dim=in_dim, out_dim=3)
sample_emb.fit(
    data={"target_probs": probs, "sample_features": features},
    params={"lr": lr, "n_step": n_step}, verbose_dir=save_dir
)

To train the importance weights $w^{(r)}$ and $v^{(k)}$,

from embeddings import ImportanceWeight

importance_weight = ImportanceWeight(*regional_features.shape)
importance_weight.estimate(
    data={"target_probs": probs, "regional_features": regional_features, "images": images},
    params={"lr": args.lr, "n_step": args.n_step}, verbose_dir=save_dir,
)

To train the regional embeddings $h^{(r)}=\Lambda f^{(r)}$,

from embeddings import RegionEmbedding

region_embedding = RegionEmbedding(in_dim=in_dim, out_dim=3)
region_embedding.fit(
    data={"target_probs": probs, "regional_features": regional_features,
          "sample_embs": sample_emb, "w_k": kernel_weights, "w_r": region_weights},
    params={"lambda_kl": args.lambda_kl, "lambda_mi": args.lambda_mi,
            "lr": args.lr, "n_step": args.n_step}, verbose_dir=save_dir
)

Usage

Codes for training models and extracting features are omitted. However, you can download the extracted feautures in the Demos section. We extract intermediate-layer features of 500 samples in the Tiny-Imagenet-10 dataset used in this paper.

In order to obtain sample embeddings and regional embeddings, you need to run the following code.

python train_sample_emb.py
python train_importance_weight.py
python train_region_emb.py

You can also specify the dataset and the DNN by yourself. For more information, please add the --help flag at the end of each command above.

Demos

We have also provided demos to reproduce experimental results presented in the paper. Raw data will be uploaded soon.

• Visualize knowledge points in different layers, visualize-knowledge-point.ipynb

  

• Visualize the change of knowlegde points before/after the adversarial attack, visualize-attack-region.ipynb

  

• Visualize trajectory of regional features throughout the attack, visualize-attack-trajectory.ipynb

  

Bibtex

If you find this work useful to your study, please cite this paper as follows.

@article{li2021visualizing,
  title={Visualizing the emergence of intermediate visual patterns in dnns},
  author={Li, Mingjie and Wang, Shaobo and Zhang, Quanshi},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={6594--6607},
  year={2021}
}