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When processing similar frames in succession, we can take advantage of thelocality of the convolution operation to reevaluate only portions of the imagethat changed from the previous frame. By saving the output of a layer ofconvolutions and calculating the change from frame to frame, we can reuseprevious activations and save computational resources that would otherwise bewasted recalculating convolutions whose outputs we have already observed. Thistechnique can be applied to many domains, such as processing videos fromstationary video cameras, studying the effects of occluding or distortingsections of images, applying convolution to multiple frames of audio or timeseries data, or playing Atari games. Furthermore, this technique can be appliedto speed up both training and inference.

Reusing Convolutional Activations from Frame to Frame to Speed up Training and Inference