Interpreting learned image patterns

Interpreting NNs that take in image data enables a new paradigm in interpretation, which is the capability to visualize exactly what a neuron is detecting. In the case of audio input data, interpreting NNs would allow us to audibly represent what a neuron is detecting, similar to how we visualize patterns in image data! Choose neurons you want to understand based on your goal and visualize the patterns it is detecting through iterative optimizing on image data to activate highly for that neuron.

Practically, however, optimizing image data based on a neuron has an issue where the resulting image often produces high-frequency patterns that are perceived to be noisy, uninterpretable, and unaesthetic. High-frequency patterns are defined to be pixels that are high in intensity and change quickly from one to the next. This is largely due to the mostly unconstrained range of values that a pixel can be represented by, and pixels in isolation are not the...