MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single Domain Generalization

• URL: http://arxiv.org/abs/2311.15906v1
• Date: Mon, 27 Nov 2023 15:13:02 GMT
• Title: MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single Domain Generalization
• Authors: Can Sun, Hao Zheng, Zhigang Hu, Liu Yang, Meiguang Zheng, Bo Xu
• Abstract summary: A novel meta-learning method based on domain enhancement and feature alignment (MetaDefa) is proposed to improve the model generalization performance. In this paper, domain-invariant features can be fully explored by focusing on similar target regions between source and augmented domains feature space. Extensive experiments on two publicly available datasets show that MetaDefa has significant generalization performance advantages in unknown multiple target domains.
• Score: 12.095382249996032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The single domain generalization(SDG) based on meta-learning has emerged as an effective technique for solving the domain-shift problem. However, the inadequate match of data distribution between source and augmented domains and difficult separation of domain-invariant features from domain-related features make SDG model hard to achieve great generalization. Therefore, a novel meta-learning method based on domain enhancement and feature alignment (MetaDefa) is proposed to improve the model generalization performance. First, the background substitution and visual corruptions techniques are used to generate diverse and effective augmented domains. Then, the multi-channel feature alignment module based on class activation maps and class agnostic activation maps is designed to effectively extract adequate transferability knowledge. In this module, domain-invariant features can be fully explored by focusing on similar target regions between source and augmented domains feature space and suppressing the feature representation of non-similar target regions. Extensive experiments on two publicly available datasets show that MetaDefa has significant generalization performance advantages in unknown multiple target domains.

Related papers

• Cross-Domain Feature Augmentation for Domain Generalization [16.174824932970004]
  We propose a cross-domain feature augmentation method named XDomainMix. Experiments on widely used benchmark datasets demonstrate that our proposed method is able to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-05-14T13:24:19Z)
• Meta-causal Learning for Single Domain Generalization [102.53303707563612]
  Single domain generalization aims to learn a model from a single training domain (source domain) and apply it to multiple unseen test domains (target domains) Existing methods focus on expanding the distribution of the training domain to cover the target domains, but without estimating the domain shift between the source and target domains. We propose a new learning paradigm, namely simulate-analyze-reduce, which first simulates the domain shift by building an auxiliary domain as the target domain, then learns to analyze the causes of domain shift, and finally learns to reduce the domain shift for model adaptation.
  arXiv  Detail & Related papers  (2023-04-07T15:46:38Z)
• Compound Domain Generalization via Meta-Knowledge Encoding [55.22920476224671]
  We introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize the multi-modal underlying distributions. We harness the prototype representations, the centroids of classes, to perform relational modeling in the embedding space. Experiments on four standard Domain Generalization benchmarks reveal that COMEN exceeds the state-of-the-art performance without the need of domain supervision.
  arXiv  Detail & Related papers  (2022-03-24T11:54:59Z)
• Domain Generalisation for Object Detection under Covariate and Concept Shift [10.32461766065764]
  Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain-specific features. An approach to domain generalisation for object detection is proposed, the first such approach applicable to any object detection architecture.
  arXiv  Detail & Related papers  (2022-03-10T11:14:18Z)
• Exploiting Domain-Specific Features to Enhance Domain Generalization [10.774902700296249]
  Domain Generalization (DG) aims to train a model, from multiple observed source domains, in order to perform well on unseen target domains. Prior DG approaches have focused on extracting domain-invariant information across sources to generalize on target domains. We propose meta-Domain Specific-Domain Invariant (mD) - a novel theoretically sound framework.
  arXiv  Detail & Related papers  (2021-10-18T15:42:39Z)
• Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification [81.30327016286009]
  We propose a novel adaptive domain-specific normalization approach (AdsNorm) for generalizable person Re-ID. In this work, we propose a novel adaptive domain-specific normalization approach (AdsNorm) for generalizable person Re-ID.
  arXiv  Detail & Related papers  (2021-05-07T02:54:55Z)
• Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation [102.42638795864178]
  We propose a principled meta-learning based approach to OCDA for semantic segmentation. We cluster target domain into multiple sub-target domains by image styles, extracted in an unsupervised manner. A meta-learner is thereafter deployed to learn to fuse sub-target domain-specific predictions, conditioned upon the style code. We learn to online update the model by model-agnostic meta-learning (MAML) algorithm, thus to further improve generalization.
  arXiv  Detail & Related papers  (2020-12-15T13:21:54Z)
• Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation [66.74638960925854]
  Partial domain adaptation (PDA) deals with a realistic and challenging problem when the source domain label space substitutes the target domain. We propose an Adaptively-Accumulated Knowledge Transfer framework (A$2$KT) to align the relevant categories across two domains.
  arXiv  Detail & Related papers  (2020-08-27T00:53:43Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)

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.