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Visualizations of video classification networks

An adaptation of lucid to visualizing features for the I3D video classification network. The video classification network pretrained on the kinetics dataset weights source.

At the time of the development of this code the n-dimensional real inverse furrier transform was not differentiable in TensorFlow. In order to bypass this a function turning the spectrum of a real signal to a hermitian array was developed, this allowed to make use of the complex inverse transform, which was differentiable, the source code could be found here.

Results

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CPPN parametrizations

video

Alpha parametrizations

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Shared image parametrizatoins

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Neuron Visualizations

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Channel Visualizations

Upper layers

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Middle layers

video0 video1 video2 video3

Lower layers

video

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Visualize 3d convolutions

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