Dynamic Domain Generalization

• URL: http://arxiv.org/abs/2205.13913v1
• Date: Fri, 27 May 2022 11:29:03 GMT
• Title: Dynamic Domain Generalization
• Authors: Zhishu Sun, Zhifeng Shen, Luojun Lin, Yuanlong Yu, Zhifeng Yang, Shicai Yang, Weijie Chen
• Abstract summary: We develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in which the model learns to twist the network parameters to adapt the data from different domains. Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains. In this way, the static model is optimized to learn domain-shared features, while the meta-adjuster is designed to learn domain-specific features.
• Score: 10.252262302724608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains. To tackle this problem, we develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in which the model learns to twist the network parameters to adapt the data from different domains. Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains. In this way, the static model is optimized to learn domain-shared features, while the meta-adjuster is designed to learn domain-specific features. To enable this process, DomainMix is exploited to simulate data from diverse domains during teaching the meta-adjuster to adapt to the upcoming agnostic target domains. This learning mechanism urges the model to generalize to different agnostic target domains via adjusting the model without training. Extensive experiments demonstrate the effectiveness of our proposed method. Code is available at: https://github.com/MetaVisionLab/DDG

Related papers

• Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification [71.08024880298613]
  We study the multi-source Domain Generalization of text classification. We propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain.
  arXiv  Detail & Related papers  (2024-09-20T07:46:21Z)
• Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis [33.86086075084374]
  Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis. We propose a Large Language Model-based Continual Learning (textttLLM-CL) model for ABSA.
  arXiv  Detail & Related papers  (2024-05-09T02:00:07Z)
• Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer [69.82229895838577]
  Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources. This motivates us to target a new and challenging setting of knowledge transfer that extends ADA from a single source domain to multiple source domains.
  arXiv  Detail & Related papers  (2023-11-21T13:12:21Z)
• Improving Domain Generalization with Domain Relations [77.63345406973097]
  This paper focuses on domain shifts, which occur when the model is applied to new domains that are different from the ones it was trained on. We propose a new approach called D$3$G to learn domain-specific models. Our results show that D$3$G consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-02-06T08:11:16Z)
• Learning to Augment via Implicit Differentiation for Domain Generalization [107.9666735637355]
  Domain generalization (DG) aims to overcome the problem by leveraging multiple source domains to learn a domain-generalizable model. In this paper, we propose a novel augmentation-based DG approach, dubbed AugLearn. AugLearn shows effectiveness on three standard DG benchmarks, PACS, Office-Home and Digits-DG.
  arXiv  Detail & Related papers  (2022-10-25T18:51:51Z)
• 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)
• Domain Adaptation via Prompt Learning [39.97105851723885]
  Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain. We introduce a novel prompt learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL)
  arXiv  Detail & Related papers  (2022-02-14T13:25:46Z)
• Dynamic Transfer for Multi-Source Domain Adaptation [82.54405157719641]
  We present dynamic transfer to address domain conflicts, where the model parameters are adapted to samples. It breaks down source domain barriers and turns multi-source domains into a single-source domain. Experimental results show that, without using domain labels, our dynamic transfer outperforms the state-of-the-art method by more than 3%.
  arXiv  Detail & Related papers  (2021-03-19T01:22:12Z)
• 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)

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.