# Explaining Neural Networks by Decoding Layer Activations

Source: [Explaining Neural Networks by Decoding Layer Activations](<https://www.sota2.com/research/paper/68fa69561e209131cea21a0b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:46.546Z

Authors: Johannes Schneider, Michalis Vlachos

Published: 2020-05-27T20:22:10.000Z

## 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}

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2005.13630>)

[PDF](<https://arxiv.org/pdf/2005.13630.pdf>)