Unsupervised domain adaptation by learning using privileged information

• URL: http://arxiv.org/abs/2303.09350v3
• Date: Wed, 12 Jun 2024 08:40:35 GMT
• Title: Unsupervised domain adaptation by learning using privileged information
• Authors: Adam Breitholtz, Anton Matsson, Fredrik D. Johansson,
• Abstract summary: We show that training-time access to side information in the form of auxiliary variables can help relax restrictions on input variables. We propose a simple two-stage learning algorithm, inspired by our analysis of the expected error in the target domain, and a practical end-to-end variant for image classification.
• Score: 6.748420131629902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Successful unsupervised domain adaptation is guaranteed only under strong assumptions such as covariate shift and overlap between input domains. The latter is often violated in high-dimensional applications like image classification which, despite this limitation, continues to serve as inspiration and benchmark for algorithm development. In this work, we show that training-time access to side information in the form of auxiliary variables can help relax restrictions on input variables and increase the sample efficiency of learning at the cost of collecting a richer variable set. As this information is assumed available only during training, not in deployment, we call this problem unsupervised domain adaptation by learning using privileged information (DALUPI). To solve this problem, we propose a simple two-stage learning algorithm, inspired by our analysis of the expected error in the target domain, and a practical end-to-end variant for image classification. We propose three evaluation tasks based on classification of entities in photos and anomalies in medical images with different types of available privileged information (binary attributes and single or multiple regions of interest). We demonstrate across these tasks that using privileged information in learning can reduce errors in domain transfer compared to baselines, be robust to spurious correlations in the source domain, and increase sample efficiency.

Related papers

• Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach [20.899013563493202]
  Domain adaptation has shown appealing performance by leveraging knowledge from a source domain with rich annotations. For a specific target task, it is cumbersome to collect related and high-quality source domains. In this paper, we propose a simple yet effective domain adaptation approach, termed as self-paced transfer classifier learning (SP-TCL)
  arXiv  Detail & Related papers  (2024-06-20T12:54:07Z)
• Domain Adaptation Principal Component Analysis: base linear method for learning with out-of-distribution data [55.41644538483948]
  Domain adaptation is a popular paradigm in modern machine learning. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) DAPCA finds a linear reduced data representation useful for solving the domain adaptation task.
  arXiv  Detail & Related papers  (2022-08-28T21:10:56Z)
• 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)
• 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)
• Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator [2.8672054847109134]
  In some target problem domains, there are not many data samples available, which could hinder the learning process. We propose a weak adaptation learning (WAL) approach that leverages unlabeled data from a similar source domain. Our experiments demonstrate the effectiveness of our approach in learning an accurate classifier with limited labeled data in the target domain.
  arXiv  Detail & Related papers  (2021-02-15T06:19:25Z)
• Self-Supervised Domain Adaptation with Consistency Training [0.2462953128215087]
  We consider the problem of unsupervised domain adaptation for image classification. We create a self-supervised pretext task by augmenting the unlabeled data with a certain type of transformation. We force the representation of the augmented data to be consistent with that of the original data.
  arXiv  Detail & Related papers  (2020-10-15T06:03:47Z)
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
  arXiv  Detail & Related papers  (2020-09-01T00:06:50Z)
• Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders [23.18781318003242]
  We present a novel Conditional Coupled Variational Autoencoder (CCVAE) which can generate synthetic target domain features for unseen classes from their source domain counterparts. Experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security.
  arXiv  Detail & Related papers  (2020-08-03T21:48:50Z)
• Adaptive Risk Minimization: Learning to Adapt to Domain Shift [109.87561509436016]
  A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. In this work, we consider the problem setting of domain generalization, where the training data are structured into domains and there may be multiple test time shifts. We introduce the framework of adaptive risk minimization (ARM), in which models are directly optimized for effective adaptation to shift by learning to adapt on the training domains.
  arXiv  Detail & Related papers  (2020-07-06T17:59:30Z)
• Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation [97.8552697905657]
  A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains. We propose Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes.
  arXiv  Detail & Related papers  (2020-04-02T03:25:05Z)
• Exploring Categorical Regularization for Domain Adaptive Object Detection [27.348272177261233]
  We propose a categorical regularization framework for domain adaptive object detection. It can be applied as a plug-and-play component on a series of Adaptive Domain Faster R-CNN methods. Our method obtains a significant performance gain over original Domain Adaptive Faster R-CNN detectors.
  arXiv  Detail & Related papers  (2020-03-20T08:53:10Z)

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.