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arXiv · 2405.06409

Visualizing Neural Network Imagination

Nevan Wichers·Victor Tao·Riccardo Volpato·Fazl Barez
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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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Nevan Wichers, Victor Tao, Riccardo Volpato, Fazl Barez. 2024-05-10. Visualizing Neural Network Imagination. https://arxiv.org/abs/2405.06409

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