{
  "id": 19546,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11648-w",
  "title": "Data augmentation in multimodal frameworks: a survey",
  "summary": "Training machine learning models with more than one data modality has enhanced predictive performance in most contexts. Thus, many recent applications of machine learning use data from different sources and forms. Multimodal data augmentation (MMDA) addresses critical challenges in multimodal learning, such as data scarcity, modality imbalance, and cross-modal alignment. This survey systematically reviews 68 state-of-the-art MMDA approaches, and, as result, proposes a taxonomy for the area. For",
  "authors": null,
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-14T00:00:00.000Z",
  "fetched_at": "2026-08-15T05:10:17.122Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/19546",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11648-w",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}