Research Blog: Feature Visualization

But neurons don’t understand the world by themselves — they work together. So we also need to understand how they interact with each other. One approach is to explore interpolations between them. What images can make them both fire, to different extents?

Here we interpolate from a neuron that seems to detect artistic patterns to a neuron that seems to detect lizard eyes:

We can also let you try adding different pairs of neurons together, to explore the possibilities for yourself:

In addition to allowing you to play around with visualizations, we explore a variety of techniques for getting feature visualization to work, and let you experiment with using them.

Techniques for visualizing and understanding neural networks are becoming more powerful. We hope our article will help other researchers apply these techniques, and give people a sense of their potential. Check it out on Distill.

Acknowledgement
We’re extremely grateful to our co-author, Ludwig Schurbert, who made incredible contributions to our paper and especially to the interactive visualizations.

Have you ever wondered what goes on inside neural networks? Feature visualization is a powerful tool for digging into neural networks and seeing how they work.

Our new article, published in Distill, does a deep exploration of feature visualization, introducing a few new tricks along the way!

Building on our work in DeepDream, and lots of work by others since, we are able to visualize what every neuron a strong vision model (GoogLeNet [1]) detects. Over the course of multiple layers, it gradually builds up abstractions: first it detects edges, then it uses those edges to detect textures, the textures to detect patterns, and the patterns to detect parts of objects….

But neurons don’t understand the world by themselves — they work together. So we also need to understand how they interact with each other. One approach is to explore interpolations between them. What images can make them both fire, to different extents?

Here we interpolate from a neuron that seems to detect artistic patterns to a neuron that seems to detect lizard eyes:

We can also let you try adding different pairs of neurons together, to explore the possibilities for yourself:

In addition to allowing you to play around with visualizations, we explore a variety of techniques for getting feature visualization to work, and let you experiment with using them.

Techniques for visualizing and understanding neural networks are becoming more powerful. We hope our article will help other researchers apply these techniques, and give people a sense of their potential. Check it out on Distill.

Acknowledgement
We’re extremely grateful to our co-author, Ludwig Schurbert, who made incredible contributions to our paper and especially to the interactive visualizations.

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