Open Domain Generalization with Domain-Augmented Meta-Learning

• URL: http://arxiv.org/abs/2104.03620v1
• Date: Thu, 8 Apr 2021 09:12:24 GMT
• Title: Open Domain Generalization with Domain-Augmented Meta-Learning
• Authors: Yang Shu, Zhangjie Cao, Chenyu Wang, Jianmin Wang, Mingsheng Long
• Abstract summary: We study a novel and practical problem of Open Domain Generalization (OpenDG) We propose a Domain-Augmented Meta-Learning framework to learn open-domain generalizable representations. Experiment results on various multi-domain datasets demonstrate that the proposed Domain-Augmented Meta-Learning (DAML) outperforms prior methods for unseen domain recognition.
• Score: 83.59952915761141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable. We study a novel and practical problem of Open Domain Generalization (OpenDG), which learns from different source domains to achieve high performance on an unknown target domain, where the distributions and label sets of each individual source domain and the target domain can be different. The problem can be generally applied to diverse source domains and widely applicable to real-world applications. We propose a Domain-Augmented Meta-Learning framework to learn open-domain generalizable representations. We augment domains on both feature-level by a new Dirichlet mixup and label-level by distilled soft-labeling, which complements each domain with missing classes and other domain knowledge. We conduct meta-learning over domains by designing new meta-learning tasks and losses to preserve domain unique knowledge and generalize knowledge across domains simultaneously. Experiment results on various multi-domain datasets demonstrate that the proposed Domain-Augmented Meta-Learning (DAML) outperforms prior methods for unseen domain recognition.

Related papers

• Domain Generalization for Domain-Linked Classes [8.738092015092207]
  In the real-world, classes may often be domain-linked, i.e. expressed only in a specific domain. We propose a Fair and cONtrastive feature-space regularization algorithm for Domain-linked DG, FOND.
  arXiv  Detail & Related papers  (2023-06-01T16:39:50Z)
• Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization [9.577254317971933]
  We argue that the domain variantions also contain useful information, ie, classification-aware information, for downstream tasks. We propose a novel paradigm called Domain Disentanglement Network (DDN) to disentangle the domain expert features from the source domain images. We also propound a new contrastive learning method to guide the domain expert features to form a more balanced and separable feature space.
  arXiv  Detail & Related papers  (2023-02-05T09:48:57Z)
• Domain-Unified Prompt Representations for Source-Free Domain Generalization [6.614361539661422]
  Domain generalization is a surefire way toward general artificial intelligence. It is difficult for existing methods to scale to diverse domains in open-world scenarios. We propose an approach based on large-scale vision-language pretraining models.
  arXiv  Detail & Related papers  (2022-09-29T16:44:09Z)
• Discovering Domain Disentanglement for Generalized Multi-source Domain Adaptation [48.02978226737235]
  A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. We propose a variational domain disentanglement (VDD) framework, which decomposes the domain representations and semantic features for each instance by encouraging dimension-wise independence.
  arXiv  Detail & Related papers  (2022-07-11T04:33:08Z)
• 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)
• 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)
• Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains [108.11746235308046]
  We propose a novel approach that learns domain-agnostic structured latent embeddings by projecting images from different domains. Our experiments on the challenging DomainNet and DomainNet-LS benchmarks show the superiority of our approach over existing methods.
  arXiv  Detail & Related papers  (2021-07-12T17:57:46Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• 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)
• Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation [65.38975706997088]
  Open set domain adaptation (OSDA) assumes the presence of unknown classes in the target domain. We show that existing state-of-the-art methods suffer a considerable performance drop in the presence of larger domain gaps. We propose a novel framework to specifically address the larger domain gaps.
  arXiv  Detail & Related papers  (2020-03-08T14:20:24Z)

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.