mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets

• URL: http://arxiv.org/abs/2012.08385v1
• Date: Tue, 15 Dec 2020 15:58:03 GMT
• Title: mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
• Authors: Rui Gong, Dengxin Dai, Yuhua Chen, Wen Li, Luc Van Gool
• Abstract summary: This paper treats this task as a multi-source domain adaptation and label unification problem. Our method consists of a partially-supervised adaptation stage and a fully-supervised adaptation stage. We verify the method on three different tasks, image classification, 2D semantic image segmentation, and joint 2D-3D semantic segmentation.
• Score: 102.62639692656458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object recognition advances very rapidly these days. One challenge is to generalize existing methods to new domains, to more classes and/or to new data modalities. In order to avoid annotating one dataset for each of these new cases, one needs to combine and reuse existing datasets that may belong to different domains, have partial annotations, and/or have different data modalities. This paper treats this task as a multi-source domain adaptation and label unification (mDALU) problem and proposes a novel method for it. Our method consists of a partially-supervised adaptation stage and a fully-supervised adaptation stage. In the former, partial knowledge is transferred from multiple source domains to the target domain and fused therein. Negative transfer between unmatched label space is mitigated via three new modules: domain attention, uncertainty maximization and attention-guided adversarial alignment. In the latter, knowledge is transferred in the unified label space after a label completion process with pseudo-labels. We verify the method on three different tasks, image classification, 2D semantic image segmentation, and joint 2D-3D semantic segmentation. Extensive experiments show that our method outperforms all competing methods significantly.

Related papers

• Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation [36.45117307751509]
  Both limited annotation and domain shift are prevalent challenges in medical image segmentation. We introduce Mixed Domain Semi-supervised medical image components (MiDSS) Our method achieves a notable 13.57% improvement in Dice score on Prostate dataset, as demonstrated on three public datasets.
  arXiv  Detail & Related papers  (2024-04-13T10:15:51Z)
• Cross-Domain Few-Shot Classification via Inter-Source Stylization [11.008292768447614]
  Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data. This paper proposes a solution that makes use of multiple source domains without the need for additional labeling costs.
  arXiv  Detail & Related papers  (2022-08-17T01:44:32Z)
• Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer [9.46677024179954]
  segmentation models do not generalize to unseen domains. We propose a light-weight style transfer framework that incorporates two class-conditional AdaIN layers. We extensively validate our approach on a synthetic sequence and further propose a challenging sequence consisting of real domains.
  arXiv  Detail & Related papers  (2022-08-12T21:30:49Z)
• Domain Adaptive Semantic Segmentation without Source Data [50.18389578589789]
  We investigate domain adaptive semantic segmentation without source data, which assumes that the model is pre-trained on the source domain. We propose an effective framework for this challenging problem with two components: positive learning and negative learning. Our framework can be easily implemented and incorporated with other methods to further enhance the performance.
  arXiv  Detail & Related papers  (2021-10-13T04:12:27Z)
• Divergence Optimization for Noisy Universal Domain Adaptation [32.05829135903389]
  Universal domain adaptation (UniDA) has been proposed to transfer knowledge learned from a label-rich source domain to a label-scarce target domain. This paper introduces a two-head convolutional neural network framework to solve all problems simultaneously.
  arXiv  Detail & Related papers  (2021-04-01T04:16:04Z)
• Your Classifier can Secretly Suffice Multi-Source Domain Adaptation [72.47706604261992]
  Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain. We present a different perspective to MSDA wherein deep models are observed to implicitly align the domains under label supervision.
  arXiv  Detail & Related papers  (2021-03-20T12:44:13Z)
• Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation [55.73722120043086]
  We develop a "Select, Label, and Mix" (SLM) framework to learn discriminative invariant feature representations for partial domain adaptation. First, we present a simple yet efficient "select" module that automatically filters out outlier source samples to avoid negative transfer. Second, the "label" module iteratively trains the classifier using both the labeled source domain data and the generated pseudo-labels for the target domain to enhance the discriminability of the latent space.
  arXiv  Detail & Related papers  (2020-12-06T19:29:32Z)
• Sparsely-Labeled Source Assisted Domain Adaptation [64.75698236688729]
  This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm. Due to the label scarcity problem, the projected clustering is conducted on both the source and target domains.
  arXiv  Detail & Related papers  (2020-05-08T15:37:35Z)
• Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation [105.96860932833759]
  State-of-the-art approaches prove that performing semantic-level alignment is helpful in tackling the domain shift issue. We propose to improve the semantic-level alignment with different strategies for stuff regions and for things. In addition to our proposed method, we show that our method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains.
  arXiv  Detail & Related papers  (2020-03-18T04:43:25Z)

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.