Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract)

Authors

  • Kira Vinogradova Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)
  • Alexandr Dibrov Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)
  • Gene Myers Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)

DOI:

https://doi.org/10.1609/aaai.v34i10.7244

Abstract

Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks. The interpretation of their predictions, however, is an active area of research. Whereas various interpretation methods have been suggested for image classification, the interpretation of image segmentation still remains largely unexplored. To that end, we propose seg-grad-cam, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.

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Published

2020-04-03

How to Cite

Vinogradova, K., Dibrov, A., & Myers, G. (2020). Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13943-13944. https://doi.org/10.1609/aaai.v34i10.7244

Issue

Section

Student Abstract Track