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TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualization

arXiv 2101.11266 ↗First posted 27 Jan 2021cs.CV cs.AIcited by 0

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Abstract (arXiv)

In this paper we introduce a tool called Principal Image Sections Mapping - PRISM, dedicated for PyTorch, but can be easily ported to other deep learning frameworks. Presented software relies on Principal Component Analysis to visualize the most significant features recognized by a given Convolutional Neural Network. Moreover, it allows to display comparative set features between images processed in the same batch, therefore PRISM can be a method well synerging with technique Explanation by Example.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 13 references that are in the map. First posted on arXiv 27 Jan 2021.

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10 of 13 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  4. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  5. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
  6. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  7. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
  8. 2015DenseCap: Fully Convolutional Localization Networks for Dense Captioningcited by 176
  9. 2015Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answeringcited by 80
  10. 2020Road Damage Detection and Classification with Detectron2 and Faster R-CNNcited by 11

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