Introducton to Circuits in Convolutional Neural Networks

Continuing our running theme this week on interpretability in deep learning neural networks, let's check out a tutorial talk by Chris Olah titled Introduction to Circuits in CNNs.  This is from a workshop at CVPR 2020.

If you are have read any of the Distil publications on feature visualization, you will find this talk interesting. 


1:  Complex Gabor filters. In reciprocal pairs.

2:  Circle detector made from curve detectors.

3.  Triangle detector made from line detectors.  Really detecting 'inflection points'.

4:  Absence of color vs color contrast detectors.

5:  Boundary detectors. Meta level higher order thing compared to normal edge detector. High-low frequency boundary, etc.

6: Clean room implementation of curve detector.


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