TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualization
This tool aids researchers and practitioners in interpreting CNN features, but it is incremental as it builds on existing visualization techniques.
The authors introduced TorchPRISM, a tool that uses Principal Component Analysis to visualize the most significant features recognized by a Convolutional Neural Network, enabling comparative feature display between images in a batch.
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.