Explaining Neural Networks by Decoding Layer Activations

• URL: http://arxiv.org/abs/2005.13630v3
• Date: Fri, 26 Feb 2021 17:11:43 GMT
• Title: Explaining Neural Networks by Decoding Layer Activations
• Authors: Johannes Schneider and Michalis Vlachos
• Abstract summary: We present a CLAssifier-DECoder' architecture (emphClaDec) which facilitates the comprehension of the output of an arbitrary layer in a neural network (NN) It uses a decoder to transform the non-interpretable representation of the given layer to a representation more similar to the domain a human is familiar with. In an image recognition problem, one can recognize what information is represented by a layer by contrasting reconstructed images of emphClaDec with those of a conventional auto-encoder(AE) serving as reference.
• Score: 3.6245632117657816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a `CLAssifier-DECoder' architecture (\emph{ClaDec}) which facilitates the comprehension of the output of an arbitrary layer in a neural network (NN). It uses a decoder to transform the non-interpretable representation of the given layer to a representation that is more similar to the domain a human is familiar with. In an image recognition problem, one can recognize what information is represented by a layer by contrasting reconstructed images of \emph{ClaDec} with those of a conventional auto-encoder(AE) serving as reference. We also extend \emph{ClaDec} to allow the trade-off between human interpretability and fidelity. We evaluate our approach for image classification using Convolutional NNs. We show that reconstructed visualizations using encodings from a classifier capture more relevant information for classification than conventional AEs. Relevant code is available at \url{https://github.com/JohnTailor/ClaDec}

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