MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization

• URL: http://arxiv.org/abs/2208.05853v3
• Date: Mon, 29 Apr 2024 06:32:01 GMT
• Title: MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization
• Authors: Lei Qi, Hongpeng Yang, Yinghuan Shi, Xin Geng,
• Abstract summary: We resort to solving the semi-supervised domain generalization task, where there are a few label information in each source domain. We propose MultiMatch, extending FixMatch to the multi-task learning framework, producing the high-quality pseudo-label for SSDG. A series of experiments validate the effectiveness of the proposed method, and it outperforms the existing semi-supervised methods and the SSDG method on several benchmark DG datasets.
• Score: 55.06956781674986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalization (DG) aims at learning a model on source domains to well generalize on the unseen target domain. Although it has achieved great success, most of existing methods require the label information for all training samples in source domains, which is time-consuming and expensive in the real-world application. In this paper, we resort to solving the semi-supervised domain generalization (SSDG) task, where there are a few label information in each source domain. To address the task, we first analyze the theory of the multi-domain learning, which highlights that 1) mitigating the impact of domain gap and 2) exploiting all samples to train the model can effectively reduce the generalization error in each source domain so as to improve the quality of pseudo-labels. According to the analysis, we propose MultiMatch, i.e., extending FixMatch to the multi-task learning framework, producing the high-quality pseudo-label for SSDG. To be specific, we consider each training domain as a single task (i.e., local task) and combine all training domains together (i.e., global task) to train an extra task for the unseen test domain. In the multi-task framework, we utilize the independent BN and classifier for each task, which can effectively alleviate the interference from different domains during pseudo-labeling. Also, most of parameters in the framework are shared, which can be trained by all training samples sufficiently. Moreover, to further boost the pseudo-label accuracy and the model's generalization, we fuse the predictions from the global task and local task during training and testing, respectively. A series of experiments validate the effectiveness of the proposed method, and it outperforms the existing semi-supervised methods and the SSDG method on several benchmark DG datasets.

Related papers

• DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding [41.49771026674969]
  We introduce a novel, practical, multi-domain multi-task setting, handling multiple domains and multiple tasks within one unified model for domain generalized point cloud understanding. Our DG-PIC does not require any model updates during the testing and can handle unseen domains and multiple tasks, textiti.e., point cloud reconstruction, denoising, and registration, within one unified model.
  arXiv  Detail & Related papers  (2024-07-11T18:21:40Z)
• NormAUG: Normalization-guided Augmentation for Domain Generalization [60.159546669021346]
  We propose a simple yet effective method called NormAUG (Normalization-guided Augmentation) for deep learning. Our method introduces diverse information at the feature level and improves the generalization of the main path. In the test stage, we leverage an ensemble strategy to combine the predictions from the auxiliary path of our model, further boosting performance.
  arXiv  Detail & Related papers  (2023-07-25T13:35:45Z)
• Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts [33.21435044949033]
  Most existing methods perform training on multiple source domains using a single model. We propose a novel framework for unsupervised test-time adaptation, which is formulated as a knowledge distillation process.
  arXiv  Detail & Related papers  (2022-10-08T02:28:10Z)
• Improving Multi-Domain Generalization through Domain Re-labeling [31.636953426159224]
  We study the important link between pre-specified domain labels and the generalization performance. We introduce a general approach for multi-domain generalization, MulDEns, that uses an ERM-based deep ensembling backbone. We show that MulDEns does not require tailoring the augmentation strategy or the training process specific to a dataset.
  arXiv  Detail & Related papers  (2021-12-17T23:21:50Z)
• META: Mimicking Embedding via oThers' Aggregation for Generalizable Person Re-identification [68.39849081353704]
  Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time. This paper presents a new approach called Mimicking Embedding via oThers' Aggregation (META) for DG ReID.
  arXiv  Detail & Related papers  (2021-12-16T08:06:50Z)
• Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization [26.255457629490135]
  We propose a novel framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels. To predict accurate pseudo-labels under domain shift, a domain-aware pseudo-labeling module is developed. Also, considering inconsistent goals between generalization and pseudo-labeling, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process.
  arXiv  Detail & Related papers  (2021-10-10T15:17:27Z)
• Multi-Target Domain Adaptation with Collaborative Consistency Learning [105.7615147382486]
  We propose a collaborative learning framework to achieve unsupervised multi-target domain adaptation. The proposed method can effectively exploit rich structured information contained in both labeled source domain and multiple unlabeled target domains.
  arXiv  Detail & Related papers  (2021-06-07T08:36:20Z)
• Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation [78.28390172958643]
  We identify two key aspects that can help to alleviate multiple domain-shifts in the multi-target domain adaptation (MTDA) We propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. When the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones.
  arXiv  Detail & Related papers  (2021-04-01T23:41:41Z)
• Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation [97.8552697905657]
  A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains. We propose Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes.
  arXiv  Detail & Related papers  (2020-04-02T03:25:05Z)

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.