Visualizing Neural Network Imagination Article Swipe

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Artificial neural network Imagination Cognitive science Computer science Psychology Artificial intelligence Cognitive psychology

Nevan Wichers , V. Tao , Riccardo Volpato , Fazl Barez ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2405.06409 · OA: W4396882269

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.