LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching

• URL: http://arxiv.org/abs/2010.06668v2
• Date: Mon, 23 Nov 2020 22:42:54 GMT
• Title: LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching
• Authors: Qun Liu, Matthew Shreve, Raja Bala
• Abstract summary: LiDAM is a semi-supervised learning approach rooted in both domain adaptation and self-paced learning. It achieves state-of-the-art performance on the CIFAR-100 dataset.
• Score: 19.606592939074737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although data is abundant, data labeling is expensive. Semi-supervised learning methods combine a few labeled samples with a large corpus of unlabeled data to effectively train models. This paper introduces our proposed method LiDAM, a semi-supervised learning approach rooted in both domain adaptation and self-paced learning. LiDAM first performs localized domain shifts to extract better domain-invariant features for the model that results in more accurate clusters and pseudo-labels. These pseudo-labels are then aligned with real class labels in a self-paced fashion using a novel iterative matching technique that is based on majority consistency over high-confidence predictions. Simultaneously, a final classifier is trained to predict ground-truth labels until convergence. LiDAM achieves state-of-the-art performance on the CIFAR-100 dataset, outperforming FixMatch (73.50% vs. 71.82%) when using 2500 labels.

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