Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks

Understand

We can better understand deep neural networks by identifying which features each of their neurons have learned to detect.

• To do so, researchers have created Deep Visualization techniques including activation maximization, which synthetically generates inputs (e.g.
• images) that maximally activate each neuron.
• A limitation of current techniques is that they assume each neuron detects only one type of feature, but we know that neurons can be multifaceted, in that they fire in response to many different types of features: for example, a grocery store class neuron must activate either for rows of produce or for a storefront.