Teacher-Student Consistency For Multi-Source Domain Adaptation

• URL: http://arxiv.org/abs/2010.10054v1
• Date: Tue, 20 Oct 2020 06:17:40 GMT
• Title: Teacher-Student Consistency For Multi-Source Domain Adaptation
• Authors: Ohad Amosy and Gal Chechik
• Abstract summary: In Multi-Source Domain Adaptation (MSDA), models are trained on samples from multiple source domains and used for inference on a different, target, domain. We propose Multi-source Student Teacher (MUST), a novel procedure designed to alleviate these issues.
• Score: 28.576613317253035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Multi-Source Domain Adaptation (MSDA), models are trained on samples from multiple source domains and used for inference on a different, target, domain. Mainstream domain adaptation approaches learn a joint representation of source and target domains. Unfortunately, a joint representation may emphasize features that are useful for the source domains but hurt inference on target (negative transfer), or remove essential information about the target domain (knowledge fading). We propose Multi-source Student Teacher (MUST), a novel procedure designed to alleviate these issues. The key idea has two steps: First, we train a teacher network on source labels and infer pseudo labels on the target. Then, we train a student network using the pseudo labels and regularized the teacher to fit the student predictions. This regularization helps the teacher predictions on the target data remain consistent between epochs. Evaluations of MUST on three MSDA benchmarks: digits, text sentiment analysis, and visual-object recognition show that MUST outperforms current SoTA, sometimes by a very large margin. We further analyze the solutions and the dynamics of the optimization showing that the learned models follow the target distribution density, implicitly using it as information within the unlabeled target data.

Related papers

• Pulling Target to Source: A New Perspective on Domain Adaptive Semantic Segmentation [80.1412989006262]
  Domain adaptive semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. We propose T2S-DA, which we interpret as a form of pulling Target to Source for Domain Adaptation.
  arXiv  Detail & Related papers  (2023-05-23T07:09:09Z)
• Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation [210.46684938698485]
  We study unsupervised domain adaptation (UDA) for semantic segmentation. We find that, by decreasing/increasing the proportion of training samples from the target domain, the 'learning ability' is strengthened/weakened. We propose a novel dual teacher-student (DTS) framework and equip it with a bidirectional learning strategy.
  arXiv  Detail & Related papers  (2023-03-16T05:04:10Z)
• 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)
• Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation [78.28390172958643]
  We identify two key aspects that can help to alleviate multiple domain-shifts in the multi-target domain adaptation (MTDA) We propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. When the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones.
  arXiv  Detail & Related papers  (2021-04-01T23:41:41Z)
• Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources [68.31273535702256]
  We propose a novel instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN) C-CycleGAN consists of three components: (1) pre-trained text encoder which encodes textual input from different domains into a continuous representation space, (2) intermediate domain generator with curriculum instance-level adaptation which bridges the gap across source and target domains, and (3) task classifier trained on the intermediate domain for final sentiment classification. We conduct extensive experiments on three benchmark datasets and achieve substantial gains over state-of-the-art DA approaches.
  arXiv  Detail & Related papers  (2020-11-17T14:50:55Z)
• Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation [65.81546955181781]
  We propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher. The student model learns the knowledge of unlabeled target data and labeled source data by two teacher models. We demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance.
  arXiv  Detail & Related papers  (2020-07-13T10:00:44Z)
• 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.