Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding

• URL: http://arxiv.org/abs/2108.02953v1
• Date: Fri, 6 Aug 2021 06:15:02 GMT
• Title: Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding
• Authors: Shengqi Huang, Wanqi Yang, Lei Wang, Luping Zhou, Ming Yang
• Abstract summary: This paper investigates a valuable setting called few-shot unsupervised domain adaptation (FS-UDA) In this setting, the source domain data are labelled, but with few-shot per category, while the target domain data are unlabelled. We develop a general UDA model to solve the few-shot labeled data per category and the domain adaptation between support and query sets.
• Score: 24.64900089320843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates a valuable setting called few-shot unsupervised domain adaptation (FS-UDA), which has not been sufficiently studied in the literature. In this setting, the source domain data are labelled, but with few-shot per category, while the target domain data are unlabelled. To address the FS-UDA setting, we develop a general UDA model to solve the following two key issues: the few-shot labeled data per category and the domain adaptation between support and query sets. Our model is general in that once trained it will be able to be applied to various FS-UDA tasks from the same source and target domains. Inspired by the recent local descriptor based few-shot learning (FSL), our general UDA model is fully built upon local descriptors (LDs) for image classification and domain adaptation. By proposing a novel concept called similarity patterns (SPs), our model not only effectively considers the spatial relationship of LDs that was ignored in previous FSL methods, but also makes the learned image similarity better serve the required domain alignment. Specifically, we propose a novel IMage-to-class sparse Similarity Encoding (IMSE) method. It learns SPs to extract the local discriminative information for classification and meanwhile aligns the covariance matrix of the SPs for domain adaptation. Also, domain adversarial training and multi-scale local feature matching are performed upon LDs. Extensive experiments conducted on a multi-domain benchmark dataset DomainNet demonstrates the state-of-the-art performance of our IMSE for the novel setting of FS-UDA. In addition, for FSL, our IMSE can also show better performance than most of recent FSL methods on miniImageNet.

Related papers

• Grounding Stylistic Domain Generalization with Quantitative Domain Shift Measures and Synthetic Scene Images [63.58800688320182]
  Domain Generalization is a challenging task in machine learning. Current methodology lacks quantitative understanding about shifts in stylistic domain. We introduce a new DG paradigm to address these risks.
  arXiv  Detail & Related papers  (2024-05-24T22:13:31Z)
• Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation [40.667166043101076]
  We propose a small adapter for rectifying diverse target domain styles to the source domain. The adapter is trained to rectify the image features from diverse synthesized target domains to align with the source domain. Our method achieves promising results on cross-domain few-shot semantic segmentation tasks.
  arXiv  Detail & Related papers  (2024-04-16T07:07:40Z)
• High-level semantic feature matters few-shot unsupervised domain adaptation [15.12545632709954]
  We propose a novel task-specific semantic feature learning method (TSECS) for FS-UDA. TSECS learns high-level semantic features for image-to-class similarity measurement. We show that the proposed method significantly outperforms SOTA methods in FS-UDA by a large margin.
  arXiv  Detail & Related papers  (2023-01-05T08:39:52Z)
• I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation [55.633859439375044]
  Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. Key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.
  arXiv  Detail & Related papers  (2023-01-03T15:19:48Z)
• Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains [36.98387822136687]
  Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. In this paper, we present a unique learning framework for domain-generalized few-shot classification. By advancing meta-learning strategies, our learning framework exploits data across multiple source domains to capture domain-invariant features.
  arXiv  Detail & Related papers  (2021-12-27T06:54:11Z)
• Disentangled Feature Representation for Few-shot Image Classification [64.40410801469106]
  We propose a novel Disentangled Feature Representation framework, dubbed DFR, for few-shot learning applications. DFR can adaptively decouple the discriminative features that are modeled by the classification branch, from the class-irrelevant component of the variation branch. In general, most of the popular deep few-shot learning methods can be plugged in as the classification branch, thus DFR can boost their performance on various few-shot tasks.
  arXiv  Detail & Related papers  (2021-09-26T09:53:11Z)
• 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)
• Few-Shot Learning as Domain Adaptation: Algorithm and Analysis [120.75020271706978]
  Few-shot learning uses prior knowledge learned from the seen classes to recognize the unseen classes. This class-difference-caused distribution shift can be considered as a special case of domain shift. We propose a prototypical domain adaptation network with attention (DAPNA) to explicitly tackle such a domain shift problem in a meta-learning framework.
  arXiv  Detail & Related papers  (2020-02-06T01:04:53Z)
• 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.