File size: 2,052 Bytes
3d9334e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
{
"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"
}
]
}
} |