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AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

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arxiv 2106.04732 v2 pith:KK4B6ZQV submitted 2021-06-08 cs.LG cs.AIcs.CV

David Berthelot , Rebecca Roelofs , Kihyuk Sohn , Nicholas Carlini , Alex Kurakin This is my paper
classification cs.LGcs.AIcs.CV
keywords adamatchdomainadaptationsemi-supervisedstate-of-the-artaccuracylearningssda
verification ladder T0 review T1 audit T2 compute T3 formal
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We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a method that unifies the tasks of unsupervised domain adaptation (UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation (SSDA). In an extensive experimental study, we compare its behavior with respective state-of-the-art techniques from SSL, SSDA, and UDA on vision classification tasks. We find AdaMatch either matches or significantly exceeds the state-of-the-art in each case using the same hyper-parameters regardless of the dataset or task. For example, AdaMatch nearly doubles the accuracy compared to that of the prior state-of-the-art on the UDA task for DomainNet and even exceeds the accuracy of the prior state-of-the-art obtained with pre-training by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by providing AdaMatch with just one labeled example per class from the target domain (i.e., the SSDA setting), we increase the target accuracy by an additional 6.1%, and with 5 labeled examples, by 13.6%.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 22 citations worldwide. Full citation record

  1. Harmonizing and Merging Source Models for CLIP-based Domain Generalization

    cs.CV 2025-06 conditional novelty 6.0 of 10

    HAM trains per-domain CLIP encoders, enriches them with confident cross-domain samples, aligns their update directions, and merges them with redundancy trimming, reaching 79.0% average accuracy on five DG benchmarks w...

  2. MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder

    cs.LG 2025-08 conditional novelty 4.0 of 10

    MoSSDA is a two-stage framework that uses MMD, mixup-based supervised contrastive learning with a momentum encoder, and a frozen-features classifier to improve semi-supervised domain adaptation for time-series classification.

  3. SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning

    cs.LG 2025-05 conditional novelty 4.0 of 10

    SST uses per-class thresholds updated once per cycle to pick pseudo-labels, reporting 84.9% ImageNet top-1 accuracy with 10% labeled data on a huge ViT.

  4. Learning from Limited and Imperfect Data

    cs.LG 2025-07 unverdicted novelty 3.0 of 10

    A doctoral thesis compiling nine peer-reviewed papers on long-tailed image generation, long-tailed recognition, semi-supervised learning, and domain adaptation.

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