{
  "abstract": "Pseudo-multiview learning (PML) improves classification by generating complementary internal representations and fusing their evidence using subjective logic. However, conventional PML incurs substantial computational costs as the number of pseudo-views increases. Multiscale grid-based PML alleviates this issue by generating lower-resolution pseudo-views through downsampling and integrating cross-scale features. Nevertheless, its unidirectional top-down feature flow is vulnerable to noise in high-resolution branches, which can propagate to lower-resolution branches and degrade classification performance. This paper proposes a noise-resilient multiscale PML framework. The proposed dual-sampler module enables each branch to aggregate complementary features from both higher- and lower-resolution branches, thereby establishing bidirectional cross-scale information flow. In addition, a noise-aware spatial-channel attention module jointly models feature discriminativeness and reliability, suppressing corrupted spatial regions and feature channels during cross-scale fusion. Experimental results on real-world datasets from diverse domains, including BreakHis at 40×, 100×, 200×, and 400× magnifications, Oxford-IIIT Pet, and Chest X-ray, demonstrate the superiority of the proposed method over baseline models in classification accuracy, convergence stability, and robustness to cross-scale Gaussian noise.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Engineering, Korea National University of Transportation, Chungbuk 27469, Republic of Korea"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dat Ngo",
      "orcid": "0000-0002-3896-648X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ee4kn6svcddgh.bib",
  "cited_by": 0,
  "doi": "10.3390/math14214001",
  "fulltext_url": null,
  "id": "ee4kn6svcddgh",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/4001",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/ee4kn6svcddgh.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:23:28Z",
      "native_id": "10.3390/math14214001",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/4001"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Improving Pseudo-Multiview Learning via Dual-Sampler Bidirectional Feature Flow",
  "title_text": "Improving Pseudo-Multiview Learning via Dual-Sampler Bidirectional Feature Flow",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T12:20:59Z",
  "url": "https://research.draconitic.com/paper/ee4kn6svcddgh",
  "venue": "Mathematics",
  "versions": []
}