Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization

Understand

Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs).

• At the same time, there are concerns that these visualizations might not accurately represent CNNs' inner workings.
• Here, we measure how much extremely activating images help humans to predict CNN activations.
• Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al.