Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues

• URL: http://arxiv.org/abs/2303.08106v1
• Date: Mon, 13 Mar 2023 15:52:30 GMT
• Title: Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
• Authors: Mohamed Akrout, Amal Feriani, Faouzi Bellili, Amine Mezghani, Ekram Hossain
• Abstract summary: Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. Most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d) This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets.
• Score: 32.61904205763364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.

Related papers

• Federated Domain Generalization: A Survey [12.84261944926547]
  In machine learning, data is often distributed across different devices, organizations, or edge nodes. In response to this challenge, there has been a surge of interest in federated domain generalization. This paper presents the first survey of recent advances in this area.
  arXiv  Detail & Related papers  (2023-06-02T07:55:42Z)
• On-Device Domain Generalization [93.79736882489982]
  Domain generalization is critical to on-device machine learning applications. We find that knowledge distillation is a strong candidate for solving the problem. We propose a simple idea called out-of-distribution knowledge distillation (OKD), which aims to teach the student how the teacher handles (synthetic) out-of-distribution data.
  arXiv  Detail & Related papers  (2022-09-15T17:59:31Z)
• Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives [16.68091981866261]
  Unsupervised domain adaptation (UDA) is proposed to counter the performance drop on data in a target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc.
  arXiv  Detail & Related papers  (2022-08-15T20:05:07Z)
• META: Mimicking Embedding via oThers' Aggregation for Generalizable Person Re-identification [68.39849081353704]
  Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time. This paper presents a new approach called Mimicking Embedding via oThers' Aggregation (META) for DG ReID.
  arXiv  Detail & Related papers  (2021-12-16T08:06:50Z)
• Towards Data-Free Domain Generalization [12.269045654957765]
  How can knowledge contained in models trained on different source data domains be merged into a single model that generalizes well to unseen target domains? Prior domain generalization methods typically rely on using source domain data, making them unsuitable for private decentralized data. We propose DEKAN, an approach that extracts and fuses domain-specific knowledge from the available teacher models into a student model robust to domain shift.
  arXiv  Detail & Related papers  (2021-10-09T11:44:05Z)
• COLUMBUS: Automated Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption [12.555885317622131]
  We address the challenging domain generalization problem, where a model trained on a set of source domains is expected to generalize well in unseen domains without exposure to their data. We propose Columbus, a method that enforces new feature discovery via a targeted corruption of the most relevant input and multi-level representations of the data.
  arXiv  Detail & Related papers  (2021-09-09T14:52:05Z)
• 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)
• Domain Generalization: A Survey [146.68420112164577]
  Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning. For the first time, a comprehensive literature review is provided to summarize the ten-year development in DG.
  arXiv  Detail & Related papers  (2021-03-03T16:12:22Z)
• Dual Distribution Alignment Network for Generalizable Person Re-Identification [174.36157174951603]
  Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID) We present a Dual Distribution Alignment Network (DDAN) which handles this challenge by selectively aligning distributions of multiple source domains. We evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID) benchmark.
  arXiv  Detail & Related papers  (2020-07-27T00:08:07Z)
• Domain Adaptive Ensemble Learning [141.98192460069765]
  We propose a unified framework termed domain adaptive ensemble learning (DAEL) to address both problems. Experiments on three multi-source UDA and two DG datasets show that DAEL improves the state of the art on both problems, often by significant margins.
  arXiv  Detail & Related papers  (2020-03-16T16:54:15Z)

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.