Visualizing Neural Network Imagination
This work addresses interpretability for neural network researchers, but it is incremental as it focuses on a simple task.
The paper tackled the problem of visualizing environment states represented in neural network hidden activations by using a decoder network and new techniques, achieving high interpretability on a simple task as measured by a quantitative metric.
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.