DeepNNK

• Non parametric, polytope interpolation framework for use with deep learning models

Python source code for paper: DeepNNK: Explaining deep models and their generalization using polytope interpolation

Code requirements

The code is tested for python3.6. Use with python2 will require some minor updates in the packages installed and source code.

• requirements.txt contains pip packages to be installed.
• Packages listed assume no GPU availability. Use faiss-gpu and tensorflow-gpu for use with GPU.

Reproducing results

• run_script.bash shows a simple set of commands needed to train, test and calibrate a regularized model on CIFAR-10 dataset.
• Modify regularize and layer_size flags to create different models.
• Set mode flag to plot to obtain predictions and associated NNK neighbors. Image ID's to be plotted is to be updated in main.py Plots obtained for model selection experiment presented in paper can be obtained by running python overfitting_study.py after setting up the appropriate paths to the models in code.
• The source code contains the models trained and used for paper in logs directory.

Citing this work

@article{shekkizhar2020deepnnk,
 title={DeepNNK: Explaining deep models and their generalization using polytope interpolation},
 author={Shekkizhar, Sarath and Ortega, Antonio},
 journal={arXiv preprint arXiv:2007.10505},
 year={2020}
}