Deep Domain-Adversarial Image Generation for Domain Generalisation

• URL: http://arxiv.org/abs/2003.06054v1
• Date: Thu, 12 Mar 2020 23:17:47 GMT
• Title: Deep Domain-Adversarial Image Generation for Domain Generalisation
• Authors: Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, Tao Xiang
• Abstract summary: Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains. We propose a novel DG approach based on emphDeep Domain-Adversarial Image Generation (DDAIG)
• Score: 115.21519842245752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains. In this paper, we propose a novel DG approach based on \emph{Deep Domain-Adversarial Image Generation} (DDAIG). Specifically, DDAIG consists of three components, namely a label classifier, a domain classifier and a domain transformation network (DoTNet). The goal for DoTNet is to map the source training data to unseen domains. This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier. By augmenting the source training data with the generated unseen domain data, we can make the label classifier more robust to unknown domain changes. Extensive experiments on four DG datasets demonstrate the effectiveness of our approach.

Related papers

• Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition [30.31153237003218]
  A novel scenario named Noisy UniDA is proposed to transfer knowledge from a labeled source domain to an unlabeled target domain. A multi-head convolutional neural network framework is proposed to address all of the challenges faced in the Noisy UniDA at once.
  arXiv  Detail & Related papers  (2023-04-20T14:18:38Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• Domain Adaptation for Real-World Single View 3D Reconstruction [1.611271868398988]
  unsupervised domain adaptation can be used to transfer knowledge from the labeled synthetic source domain to the unlabeled real target domain. We propose a novel architecture which takes advantage of the fact that in this setting, target domain data is unsupervised with regards to the 3D model but supervised for class labels. Results are performed with ShapeNet as the source domain and domains within the Object Domain Suite (ODDS) dataset as the target.
  arXiv  Detail & Related papers  (2021-08-24T22:02:27Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• 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)
• Robust Domain-Free Domain Generalization with Class-aware Alignment [4.442096198968069]
  Domain-Free Domain Generalization (DFDG) is a model-agnostic method to achieve better generalization performance on the unseen test domain. DFDG uses novel strategies to learn domain-invariant class-discriminative features. It obtains competitive performance on both time series sensor and image classification public datasets.
  arXiv  Detail & Related papers  (2021-02-17T17:46:06Z)
• Generation for adaption: a Gan-based approach for 3D Domain Adaption inPoint Cloud [10.614067060304919]
  Unsupervised domain adaptation (UDA) seeks to overcome such a problem without target domain labels. We propose a method that use a Generative adversarial network to generate synthetic data from the source domain. Experiments show that our approach performs better than other state-of-the-art UDA methods in three popular 3D object/scene datasets.
  arXiv  Detail & Related papers  (2021-02-15T07:24:10Z)
• Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
  Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
  arXiv  Detail & Related papers  (2020-03-04T23:25:09Z)
• Multi-source Domain Adaptation for Visual Sentiment Classification [92.53780541232773]
  We propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN) To handle data from multiple source domains, MSGAN learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.
  arXiv  Detail & Related papers  (2020-01-12T08:37:42Z)

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.