Towards Learning Convolutions from Scratch

NIPS 2020, 2020.

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We studied the inductive bias of convolutional networks through empirical investigations and Minimum Description Length theory

Abstract:

Convolution is one of the most essential components of architectures used in computer vision. As machine learning moves towards reducing the expert bias and learning it from data, a natural next step seems to be learning convolution-like structures from scratch. This, however, has proven elusive. For example, current state-of-the-art ar...More

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