Contrastive Regularization for Semi-Supervised Learning

• URL: http://arxiv.org/abs/2201.06247v1
• Date: Mon, 17 Jan 2022 07:20:11 GMT
• Title: Contrastive Regularization for Semi-Supervised Learning
• Authors: Doyup Lee, Sungwoong Kim, Ildoo Kim, Yeongjae Cheon, Minsu Cho, Wook-Shin Han
• Abstract summary: We propose contrastive regularization to improve both efficiency and accuracy of the consistency regularization by well-clustered features of unlabeled data. Our method also shows robust performance on open-set semi-supervised learning where unlabeled data includes out-of-distribution samples.
• Score: 46.020125061295886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance. In this study, we analyze that the consistency regularization restricts the propagation of labeling information due to the exclusion of samples with unconfident pseudo-labels in the model updates. Then, we propose contrastive regularization to improve both efficiency and accuracy of the consistency regularization by well-clustered features of unlabeled data. In specific, after strongly augmented samples are assigned to clusters by their pseudo-labels, our contrastive regularization updates the model so that the features with confident pseudo-labels aggregate the features in the same cluster, while pushing away features in different clusters. As a result, the information of confident pseudo-labels can be effectively propagated into more unlabeled samples during training by the well-clustered features. On benchmarks of semi-supervised learning tasks, our contrastive regularization improves the previous consistency-based methods and achieves state-of-the-art results, especially with fewer training iterations. Our method also shows robust performance on open-set semi-supervised learning where unlabeled data includes out-of-distribution samples.

Related papers

• Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning [81.83013974171364]
  Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance. We propose a dual-perspective method to generate high-quality pseudo-labels.
  arXiv  Detail & Related papers  (2024-07-26T09:33:53Z)
• AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning [5.0823084858349485]
  We present a novel SSL algorithm named AllMatch, which achieves improved pseudo-label accuracy and a 100% utilization ratio for the unlabeled data. The results demonstrate that AllMatch consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-22T06:59:52Z)
• SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning [101.86916775218403]
  This paper revisits the popular pseudo-labeling methods via a unified sample weighting formulation. We propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.
  arXiv  Detail & Related papers  (2023-01-26T03:53:25Z)
• Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning [10.396596055773012]
  We propose a novel complementary learning approach to enhance test-time adaptation. In test-time adaptation tasks, information from the source domain is typically unavailable. We highlight that the risk function of complementary labels agrees with their Vanilla loss formula.
  arXiv  Detail & Related papers  (2023-01-15T03:36:33Z)
• Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification [80.98291772215154]
  Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels. We propose a Neighbour Consistency guided Pseudo Label Refinement framework.
  arXiv  Detail & Related papers  (2022-11-30T09:39:57Z)
• Controller-Guided Partial Label Consistency Regularization with Unlabeled Data [49.24911720809604]
  We propose a controller-guided consistency regularization at both the label-level and representation-level. We dynamically adjust the confidence thresholds so that the number of samples of each class participating in consistency regularization remains roughly equal to alleviate the problem of class-imbalance.
  arXiv  Detail & Related papers  (2022-10-20T12:15:13Z)
• Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE) [2.33877878310217]
  We present a new approach to suppress confounding errors through a method we describe as Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE) Our results show that SCOPE greatly improves semi-supervised classification accuracy over a baseline, and furthermore when combined with consistency regularization achieves the highest reported accuracy for the semi-supervised CIFAR-10 classification task using 250 and 4000 labeled samples.
  arXiv  Detail & Related papers  (2022-06-28T19:32:50Z)
• Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels [26.542718087103665]
  SemiMatch is a semi-supervised solution for establishing dense correspondences across semantically similar images. Our framework generates the pseudo-labels using the model's prediction itself between source and weakly-augmented target, and uses pseudo-labels to learn the model again between source and strongly-augmented target. In experiments, SemiMatch achieves state-of-the-art performance on various benchmarks, especially on PF-Willow by a large margin.
  arXiv  Detail & Related papers  (2022-03-30T03:52:50Z)
• Semi-supervised Long-tailed Recognition using Alternate Sampling [95.93760490301395]
  Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. We propose a new recognition setting, namely semi-supervised long-tailed recognition. We demonstrate significant accuracy improvements over other competitive methods on two datasets.
  arXiv  Detail & Related papers  (2021-05-01T00:43:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.