We propose regularizing the empirical loss for semi-supervised learning by
acting on both the input (data) space, and the weight (parameter) space. We
show that the two are not equivalent, and in fact are complementary, one
affecting the minimality of the resulting representation, the other
insensitivity to nuisance variability. 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 achieves
comparable performance to the state-of-the-art without resorting to heavy data
augmentation, using a relatively simple architecture.

本文提出了一种半监督学习的正则化方法，同时作用于输入空间和权重空间，通过组合已知输入空间平滑和基于 min-max 最优化的权重空间平滑，提出了 Adversarial Block Coordinate Descent 算法，使用相对简单的架构，无需重型数据增强，实现了与最先进技术可比的性能。