Skip to content

csinva/hierarchical-dnn-interpretations

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
acd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Hierarchical neural-net interpretations (ACD) 🧠

Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Official code for Hierarchical interpretations for neural network predictions (ICLR 2019 pdf).

DocumentationDemo notebooks

Note: this repo is actively maintained. For any questions please file an issue.

examples/documentation

  • installation: pip install acd (or clone and run python setup.py install)
  • examples: the reproduce_figs folder has notebooks with many demos
  • src: the acd folder contains the source for the method implementation
  • allows for different types of interpretations by changing hyperparameters (explained in examples)
  • all required data/models/code for reproducing are included in the dsets folder
Inspecting NLP sentiment models Detecting adversarial examples Analyzing imagenet models

notes on using ACD on your own data

  • the current CD implementation often works out-of-the box, especially for networks built on common layers, such as alexnet/vgg/resnet. However, if you have custom layers or layers not accessible in net.modules(), you may need to write a custom function to iterate through some layers of your network (for examples see cd.py).
  • to use baselines such build-up and occlusion, replace the pred_ims function by a function, which gets predictions from your model given a batch of examples.

related work

  • CDEP (ICML 2020 pdf, github) - penalizes CD / ACD scores during training to make models generalize better
  • TRIM (ICLR 2020 workshop pdf, github) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies)
  • PDR framework (PNAS 2019 pdf) - an overarching framewwork for guiding and framing interpretable machine learning
  • DAC (arXiv 2019 pdf, github) - finds disentangled interpretations for random forests
  • Baseline interpretability methods - the file scores/score_funcs.py also contains simple pytorch implementations of integrated gradients and the simple interpration technique gradient * input

reference

  • feel free to use/share this code openly
  • if you find this code useful for your research, please cite the following:
@inproceedings{
   singh2019hierarchical,
   title={Hierarchical interpretations for neural network predictions},
   author={Chandan Singh and W. James Murdoch and Bin Yu},
   booktitle={International Conference on Learning Representations},
   year={2019},
   url={https://openreview.net/forum?id=SkEqro0ctQ},
}