Input and Weight Space Smoothing for Semi-Supervised Learning

Safa Cicek, Stefano Soatto; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We propose a method to perform such smoothing, which combines known input-space smoothing with a novel weight-space smoothing, based on a min-max (adversarial) optimization. The resulting Adversarial Block Coordinate Descent (ABCD) algorithm performs gradient ascent with a small learning rate for a random subset of the weights, and standard gradient descent on the remaining weights in the same mini-batch. It is simple to implement and achieves state-of-the-art performance.

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[bibtex]
@InProceedings{Cicek_2019_ICCV,
author = {Cicek, Safa and Soatto, Stefano},
title = {Input and Weight Space Smoothing for Semi-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}