DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation

• URL: http://arxiv.org/abs/2202.12350v1
• Date: Thu, 24 Feb 2022 20:25:46 GMT
• Title: DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation
• Authors: Nitay Calderon and Eyal Ben-David and Amir Feder and Roi Reichart
• Abstract summary: We propose a controllable generation approach to deal with domain adaptation challenges. Our model outperforms strong baselines and improves the accuracy of a state-of-the-art unsupervised DA algorithm.
• Score: 19.834322227607917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controllable generation approach in order to deal with this domain adaptation (DA) challenge. Given an input text example, our DoCoGen algorithm generates a domain-counterfactual textual example (D-con) - that is similar to the original in all aspects, including the task label, but its domain is changed to a desired one. Importantly, DoCoGen is trained using only unlabeled examples from multiple domains - no NLP task labels or parallel pairs of textual examples and their domain-counterfactuals are required. We use the D-cons generated by DoCoGen to augment a sentiment classifier in 20 DA setups, where source-domain labeled data is scarce. Our model outperforms strong baselines and improves the accuracy of a state-of-the-art unsupervised DA algorithm.

Related papers

• Dynamic Instance Domain Adaptation [109.53575039217094]
  Most studies on unsupervised domain adaptation assume that each domain's training samples come with domain labels. We develop a dynamic neural network with adaptive convolutional kernels to generate instance-adaptive residuals to adapt domain-agnostic deep features to each individual instance. Our model, dubbed DIDA-Net, achieves state-of-the-art performance on several commonly used single-source and multi-source UDA datasets.
  arXiv  Detail & Related papers  (2022-03-09T20:05:54Z)
• Domain Adaptation via Prompt Learning [39.97105851723885]
  Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain. We introduce a novel prompt learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL)
  arXiv  Detail & Related papers  (2022-02-14T13:25:46Z)
• Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection [86.98573522894961]
  We propose a framework that generalizes the components commonly used by Unsupervised Domain Adaptation (UDA) algorithms for detection. Specifically, we propose a novel UDA algorithm, ViSGA, that leverages the best design choices and introduces a simple but effective method to aggregate features at instance-level. We show that both similarity-based grouping and adversarial training allows our model to focus on coarsely aligning feature groups, without being forced to match all instances across loosely aligned domains.
  arXiv  Detail & Related papers  (2021-10-04T13:09:56Z)
• CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation [44.06904757181245]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to a different unlabeled target domain. One fundamental problem for the category level based UDA is the production of pseudo labels for samples in target domain. We design a two-way center-aware labeling algorithm to produce pseudo labels for target samples. Along with the pseudo labels, a weight-sharing triple-branch transformer framework is proposed to apply self-attention and cross-attention for source/target feature learning and source-target domain alignment.
  arXiv  Detail & Related papers  (2021-09-13T17:59:07Z)
• Cross-domain Contrastive Learning for Unsupervised Domain Adaptation [108.63914324182984]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. We build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets.
  arXiv  Detail & Related papers  (2021-06-10T06:32:30Z)
• Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation [91.58443042554903]
  We propose an end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA) PCS not only performs cross-domain low-level feature alignment, but it also encodes and aligns semantic structures in the shared embedding space across domains. Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 10.5%, 3.5%, 9.0%, and 13.2% on Office, Office-Home, VisDA-2017, and DomainNet, respectively.
  arXiv  Detail & Related papers  (2021-03-31T02:07:42Z)
• PADA: A Prompt-based Autoregressive Approach for Adaptation to Unseen Domains [19.682729518136142]
  PADA: A Prompt-based Autoregressive Domain Adaptation algorithm, based on the T5 model. We present PADA: A Prompt-based Autoregressive Domain Adaptation algorithm, based on the T5 model. In experiments with two tasks, PADA strongly outperforms state-of-the-art approaches and additional strong baselines.
  arXiv  Detail & Related papers  (2021-02-24T11:02:29Z)
• Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency [93.89773386634717]
  Visual domain adaptation involves learning to classify images from a target visual domain using labels available in a different source domain. We show that in the presence of a few target labels, simple techniques like self-supervision (via rotation prediction) and consistency regularization can be effective without any adversarial alignment to learn a good target classifier. Our Pretraining and Consistency (PAC) approach, can achieve state of the art accuracy on this semi-supervised domain adaptation task, surpassing multiple adversarial domain alignment methods, across multiple datasets.
  arXiv  Detail & Related papers  (2021-01-29T18:40:17Z)
• Deep Domain-Adversarial Image Generation for Domain Generalisation [115.21519842245752]
  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)
  arXiv  Detail & Related papers  (2020-03-12T23:17:47Z)
• Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift [20.533804144992207]
  Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting. We propose a new assumption, generalized label shift ($GLS$), to improve robustness against mismatched label distributions. Our algorithms outperform the base versions, with vast improvements for large label distribution mismatches.
  arXiv  Detail & Related papers  (2020-03-10T00:35:23Z)

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.