Visualizing Neural Network Imagination

• URL: http://arxiv.org/abs/2405.06409v1
• Date: Fri, 10 May 2024 11:43:35 GMT
• Title: Visualizing Neural Network Imagination
• Authors: Nevan Wichers, Victor Tao, Riccardo Volpato, Fazl Barez,
• Abstract summary: In certain situations, neural networks will represent environment states in their hidden activations. Our goal is to visualize what environment states the networks are representing. We define a quantitative interpretability metric and use it to demonstrate that hidden states can be highly interpretable.
• Score: 2.1749194587826026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In certain situations, neural networks will represent environment states in their hidden activations. Our goal is to visualize what environment states the networks are representing. We experiment with a recurrent neural network (RNN) architecture with a decoder network at the end. After training, we apply the decoder to the intermediate representations of the network to visualize what they represent. We define a quantitative interpretability metric and use it to demonstrate that hidden states can be highly interpretable on a simple task. We also develop autoencoder and adversarial techniques and show that benefit interpretability.

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