Microscope systematically visualizes every neuron in several commonly studied vision models, and makes all of those neurons linkable. We hope this will support the interpretability community in several ways:
- Although these models and visualizations are already open source (we help maintain the lucid library(opens in a new window), which is used to generate all the visualizations in Microscope) visualizing neurons is tedious. Microscope changes the feedback loop of exploring neurons from minutes to seconds. This quick feedback loop has been essential for us in discovering unexpected features like high-low frequency detectors in the ongoing circuits project(opens in a new window).
- Making models and neurons linkable allows immediate scrutiny and further exploration of research making claims about those neurons. It also removes potential confusion about which model and neuron is being discussed (which of the five versions of InceptionV1 are we talking about again?). This is really helpful for collaboration, especially when researchers are at different institutions.
- One of the wonderful things about interpretability as an area of ML is how accessible it is. Compared to many other areas, it requires comparatively little access to compute. But systematically visualizing neural networks can still take hundreds of GPU hours. We hope that, by sharing our visualizations, we can help keep interpretability highly accessible.
Just as biologists often focus on the study of a few “model organisms,” Microscope focuses on exploring a small number of models in detail. Our initial release includes nine frequently studied vision models, along with several visualization techniques we’ve found particularly useful in studying them. We plan to expand to other models and techniques in the coming months.
We’re excited to see how the community will use Microscope, and we encourage you to reuse these assets. In particular, we think it has a lot of potential in supporting the Circuits collaboration(opens in a new window)—a project to reverse engineer neural networks by analyzing individual neurons and their connections—or similar work.
