Welcome to Barlow

Barlow is a tool for identifying the failure modes for a given neural network. To achieve this, Barlow first creates a group of images such that all images in the given group have the same predicted class or the label. Then, it uses a (possibly separate) robust model to extract the embedding for all images in the group. The embedding encodes the human-interpretable visual attributes present in the image. It then learns a decision tree using this embedding that predicts failure on the neural network being inspected for failure.

Prerequisites

• Python 3.7+
• Pytorch 1.6+
• scikit-learn 0.23+
• scipy 1.5+
• matplotlib 3.2+
• robustness

Setup

• Load the Robust Resnet-50 model using the command given below:
  wget -O models/robust_resnet50.pth https://www.dropbox.com/s/knf4uimlqsi1yz8/imagenet_l2_3_0.pt?dl=0
• Specify IMAGENET_PATH in failure_explanation.ipynb to the ImageNet dataset (The last folder in IMAGENET_PATH should be /ILSVRC2012/).

Running on ImageNet classes

• Run failure_explanation.ipynb to find failure modes of various ImageNet classes.
• Specify class_index, prediction, model_name in the jupyter notebook to visualize features in Section F of the paper.
• Example for class_index = 845, grouping = prediction, model_name = standard given below:

Running on custom dataset

• Load your custom dataset into the folder sample_data/
• Specify the predictions, labels, filenames as in the file metadata.csv.
• Run failure_explanation_sample.ipynb to identify example failure modes for the given set of images (in sample_data/) using the robust model located in models/.
• For the images with label water jug, when feature[1378] (visually identified as 'water jug handle') is less than 0.089335, error rate increases to 100.0% from 50.0%, i.e an increase of 50.0% in the failure rate.

Citation

@inproceedings{singlaCVPR2021,
  title     = {Understanding Failures of Deep Networks via Robust Feature Extraction},
  author    = {Sahil Singla and Besmira Nushi and Shital Shah and Ece Kamar and Eric Horvitz},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2021},
  publisher = {Computer Vision Foundation / {IEEE}},
  year      = {2021},
  url       = {https://openaccess.thecvf.com/content/CVPR2021/papers/Singla_Understanding_Failures_of_Deep_Networks_via_Robust_Feature_Extraction_CVPR_2021_paper.pdf},
}