DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization

• URL: http://arxiv.org/abs/2404.13848v1
• Date: Mon, 22 Apr 2024 03:15:42 GMT
• Title: DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization
• Authors: Juncheng Yang, Zuchao Li, Shuai Xie, Wei Yu, Shijun Li,
• Abstract summary: We propose a Dual-Stream Separation and Reconstruction Network, dubbed DSDRNet. It is a disentanglement-reconstruction approach that integrates features of both inter-instance and intra-instance through dual-stream fusion. Experiments on four benchmark datasets demonstrate that DSDRNet outperforms other popular methods in terms of domain generalization capabilities.
• Score: 26.19333812906363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain generalization faces challenges due to the distribution shift between training and testing sets, and the presence of unseen target domains. Common solutions include domain alignment, meta-learning, data augmentation, or ensemble learning, all of which rely on domain labels or domain adversarial techniques. In this paper, we propose a Dual-Stream Separation and Reconstruction Network, dubbed DSDRNet. It is a disentanglement-reconstruction approach that integrates features of both inter-instance and intra-instance through dual-stream fusion. The method introduces novel supervised signals by combining inter-instance semantic distance and intra-instance similarity. Incorporating Adaptive Instance Normalization (AdaIN) into a two-stage cyclic reconstruction process enhances self-disentangled reconstruction signals to facilitate model convergence. Extensive experiments on four benchmark datasets demonstrate that DSDRNet outperforms other popular methods in terms of domain generalization capabilities.

Related papers

• Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation [40.12863532926691]
  Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations.<n> encoder features often entangle domain-relevant and category-relevant information, limiting both generalization and rapid adaptation to new domains.<n>We propose a Divide-and-Conquer Decoupled Network (DCDNet) to address this issue.
  arXiv  Detail & Related papers  (2025-11-11T03:29:57Z)
• From Limited Labels to Open Domains: An Efficient Learning Paradigm for UAV-view Geo-Localization [2.502228766495945]
  Traditional UAV-view Geo-Localization (UVGL) supervised paradigms are constrained by the strict reliance on paired data for positive sample selection. We propose a novel cross-domain invariance knowledge transfer network (CDIKTNet) Our method achieves state-of-the-art performance under fully supervised conditions.
  arXiv  Detail & Related papers  (2025-03-10T16:46:43Z)
• A Universal Degradation-based Bridging Technique for Domain Adaptive Semantic Segmentation [32.1141872384702]
  DiDA is a universal degradation-based bridging technique formalized as a diffusion forward process. As a plug-and-play solution, DiDA supports various degradation operations and seamlessly integrates with existing UDA methods.
  arXiv  Detail & Related papers  (2024-12-13T18:35:27Z)
• Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts [56.57141696245328]
  In open-world scenarios, where both novel classes and domains may exist, an ideal segmentation model should detect anomaly classes for safety. Existing methods often struggle to distinguish between domain-level and semantic-level distribution shifts.
  arXiv  Detail & Related papers  (2024-11-06T11:03:02Z)
• A Pairwise DomMix Attentive Adversarial Network for Unsupervised Domain Adaptive Object Detection [18.67853854539245]
  Unsupervised Domain Adaptive Object Detection (DAOD) could adapt a model trained on a source domain to an unlabeled target domain for object detection. We propose a pairwise attentive adversarial network with a Domain Mixup (DomMix) module to mitigate the aforementioned challenges.
  arXiv  Detail & Related papers  (2024-07-03T06:25:20Z)
• Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space [4.871119861180455]
  We introduce a two-phase representation learning technique using multi-task learning. We disentangle the latent space by minimizing the mutual information between the prior and latent space. We assess the model's efficacy across multiple cybersecurity datasets.
  arXiv  Detail & Related papers  (2023-12-28T17:24:13Z)
• Adversarial Bi-Regressor Network for Domain Adaptive Regression [52.5168835502987]
  It is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross-domain regression model.
  arXiv  Detail & Related papers  (2022-09-20T18:38:28Z)
• 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)
• Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data [8.645556125521246]
  Generalising semi-supervised learning to cross-domain data is of high importance to improve model robustness. The AHDC consists of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual Consistency learning module (HDC) We demonstrate the performance of our proposed AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from different centres and a 3D CT dataset.
  arXiv  Detail & Related papers  (2021-09-17T02:15:10Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation [74.3349233035632]
  Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) do not consider an inter-class variation within the target domain itself or estimated category. We introduce a learnable clustering module, and a novel domain adaptation framework called cross-domain grouping and alignment. Our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
  arXiv  Detail & Related papers  (2020-12-15T11:36:21Z)
• Bi-Directional Generation for Unsupervised Domain Adaptation [61.73001005378002]
  Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. Conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. We propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
  arXiv  Detail & Related papers  (2020-02-12T09:45:39Z)

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.