GitHub - csinva/hierarchical-dnn-interpretations: Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019) (original) (raw)

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).

Documentation •Demo 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}, }