{
  "abstract": "Deep joint source--channel coding (Deep JSCC) can suffer from domain shift between training and deployment data. We study single-source domain adaptation for image classification over wireless channels. A Gaussian example shows that improving channel quality does not necessarily improve target-domain accuracy under a fixed source-trained classifier. We further proposed an information objective to reduce domain information while preserving class information. Motivated by the analysis, we develop a domain-adaptive Deep JSCC method that combines class-wise adversarial alignment with pseudo-label supervised contrastive learning. The former uses all target samples to reduce class-conditional domain differences, while the latter selects high-confidence target samples to obtain class-discriminative received representations. We relate these losses to the information objective, and analyze the effects of pseudo-label errors and confidence-based selection. Experiments on SVHN--MNIST and PACS over AWGN and Rayleigh channels demonstrate improved target-domain classification without additional inference-time networks. The proposed method achieves 98.15\\% accuracy on SVHN $\\rightarrow$ MNIST at 10~dB CSNR.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.28907",
  "author_total": 4,
  "authors": [
    {
      "name": "Yishen Li"
    },
    {
      "name": "Xuechen Chen"
    },
    {
      "name": "Xiaoheng Deng"
    },
    {
      "name": "Hao Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/39cfv516e3bth.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.28907",
  "fulltext_url": null,
  "id": "39cfv516e3bth",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.28907",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "1702|Domain Adaptation and Few-Shot Learning"
    ]
  ],
  "openalex": "W7172300789",
  "pdf_url": "https://arxiv.org/pdf/2607.28907",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/39cfv516e3bth.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:28Z",
      "native_id": "2607.28907",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.28907"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification",
  "title_text": "Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:28Z",
  "url": "https://research.draconitic.com/paper/39cfv516e3bth",
  "venue": "arXiv",
  "versions": []
}