{
  "id": "rkehoAVtvS",
  "title": "Adversarial Paritial Multi-label Learning",
  "track": "main",
  "author": "Yan Yan;Yuhong Guo",
  "pdf": "https://openreview.net/pdf?id=rkehoAVtvS",
  "keyword": "",
  "abstract": "Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community. In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encoder-decoder framework for partial multi-label learning. The PML-GAN model uses a disambiguation network to identify noisy labels and uses a multi-label prediction network to map the training instances to the disambiguated label vectors, while deploying a generative adversarial network as an inverse mapping from label vectors to data samples in the input feature space. The learning of the overall model corresponds to a minimax adversarial game, which enhances the correspondence of input features with the output labels. Extensive experiments are conducted on multiple datasets, while the proposed model demonstrates the state-of-the-art performance for partial multi-label learning.",
  "conference": {
    "name": "ICLR",
    "year": 2020
  },
  "template": null,
  "category": "01. Deep Learning Architectures and Methods",
  "is_done": true,
  "timestamp": "2025-08-04T05:24:17.601726",
  "rule_paper_possible_url": null,
  "github_base": null,
  "llm_believed_url": null,
  "rule_base_possible_url": null,
  "confirmed_url": null,
  "Internet_fail": null,
  "html_fail": null,
  "citation_data": {
    "original_title": "Adversarial Paritial Multi-label Learning",
    "matched_title": "Adversarial Paritial Multi-label Learning",
    "citation_count": 0,
    "similarity": 1.0,
    "source": "semantic_scholar",
    "year": 2019,
    "authors": [
      {
        "authorId": null,
        "name": "Yan Yan"
      },
      {
        "authorId": "2293820699",
        "name": "Yuhong Guo"
      }
    ]
  }
}