Attribute - Python library for Neural Network Interpretability

Final capstone presentation for Attribute - a python library for easy to use methods to interpret a TensorFlow or Pytorch based Neural Network.

Transcript

1. Attribute : Neural Network Interpretability in Pytorch and TensorFlow Plaksha Tech Leaders Fellowship Capstone Project Praveen Sridhar Mentored by Nikhil Narayan, Lead Data Scientist, mfine.co
2. Introduction Predictions of Deep Learning models are difficult to explain and interpret Regulated industries like medical and finance industries often fall short from using Deep learning techniques due to this. Techniques for Interpretability help users to : • Trust and understand why predictions are made in a certain way • Provides accountability for usage in important decision making
3. Interpretability in the Medical Industry [Mukundhan et al. Google Research]
4. Objective 1. Research and implement algorithms for Neural Network Interpretability in the domain of Computer Vision
5. Literature Review
6. Pixel-space Attribution [Srinivas et al. NIPS 2019]
7. Saliency Heatmaps Grad CAM Full Grad Integrated Gradients Original Image [Srinivas et al. NIPS 2019]
8. Neuron Visualization Gradient Ascent Integrated Gradients SUMMIT [Hohman et al.]
9. Implementation
10. Attribute Python Library for Neural Network Interpretability in PyTorch and TensorFlow
11. Design • Unified API for different types of techniques • Benchmarking for comparisons
12. Details of Algorithms implemented
13. Gradient Attribution CEO In this, the gradient of output with respect to input image is taken as the attribution map
14. Smooth Gradients CEO Smoothed version of gradient attribution. Random noise is added to the input image and averaged to get the final attribution
15. Integrated Gradients CEO
16. Integrated Gradients (continued) CEO
17. GradCAM CEO Generalization of CAM (Class Activation Maps) to arbitrary architectures. CAM works only for networks with last layer as an Global Average Pooling layer Alpha = Weights[:, class_index] # (512,) FeatureMaps = getLastConvLayer() # (7,7,512) CAM = Alpha * FeatureMaps # (7,7) Upsample to original image size and overlay In Grad CAM, the equivalent to global average pooling is performed on the gradients of the output with respect to the feature maps Aij
18. FullGrad CEO FullGrad saliency uses a decomposition of the neural network into input sensitivity and neuron sensitivity components to give an exact representation of attribution
19. FullGrad CEO Official implementation requires modification of the network definition to get intermediate gradients and biases
20. FullGrad CEO In the attribute library, the implementation is flexible and automatically extracts the required intermediate gradients and bias values using internal PyTorch APIs
21. Sample Outputs
22. Sample Outputs GradCAM
23. Benchmarks
24. Conclusion The required Neural Network Library was implemented in both PyTorch and TensorFlow supporting the following algorithms • Gradient Attribution • Integrated Gradients • Smoothed Gradients • GradCAM • FullGrad The library features a consistent API across different techniques as well as a benchmarking utility
25. Future Scope The project can be expanded to • More techniques for interpretability, especially neuron visualisation • Support other modalities like Text and Speech A possible line of research was found while testing FullGrad technique, where it was discovered that heat maps produced were not very class discriminative. A combination of ideas from GradCAM could potentially solve this.
26. Thank you