Visualizing Neural Networks
Summary
Visualizing neural networks is a crucial aspect of AI alignment research, aiming to provide insights into the inner workings of complex models like convolutional neural networks (CNNs). Techniques such as activation maximization and feature visualization have been developed to help researchers and practitioners understand how different layers and units within a network respond to specific stimuli. While these methods can offer valuable information about the function of intermediate feature layers and the operation of classifiers, their effectiveness in providing causal understanding of unit activations remains debatable. Studies have shown that feature visualizations can marginally improve human performance in predicting the effects of image occlusions on unit activations compared to baseline performance. However, they do not significantly outperform simpler visualization methods like dataset samples. As such, the quest for more effective and interpretable visualization techniques continues, with the goal of enhancing our understanding of neural network behavior and improving AI alignment.