02/02/2021

Adversarial Partial Multi-Label Learning with Label Disambiguation

Yan Yan, Yuhong Guo

Keywords:

Abstract Paper Similar Papers

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 irrelevant labels and uses a multi-label prediction network to map the training instances to their 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 in a bi-directional mapping. Extensive experiments are conducted on both synthetic and real-world partial multi-label datasets, while the proposed model demonstrates the state-of-the-art performance.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38949017
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 8:51 
 0
 0
 0
 0
 20:16 
 0
 0
 0
 0
 15:00 
 0
 0
 0
 0
 13:31 
 0
 0
 0
 0
 14:37 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 13:45 
 0
 0
 0
 0
 3:06 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 15:39 
 0
 0
 0
 0
 15:11 
 0
 0
 0
 0
 5:21 
 0
 0
 0
 1
 5:13 
 0
 0
 0
 0
 10:30 
 0
 0
 0
 0
 15:01 
 0
 0
 0
 0
 4:32 
 0
 0
 0
 0
 17:23 
 0
 0
 0
 0
 15:13 
 0
 0
 0
 0
 3:25 
 0
 0
 0
 0
 5:16 
 0
 0
 0
 0
 11:27 
 0
 0
 0
 0
 3:19 
 0
 0
 0
 0
 16:52 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 8:40 
 0
 0
 0
 0
 10:39 
 0
 0
 0
 0
 5:36 
 0
 0
 0
 0
 5:00 
 0
 0
 0
 0
 15:09 
 0
 0
 0
 0
 13:09 
 0
 0
 0
 0
 6:02 
 0
 0
 0
 0
 2:57 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 3:27 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 12:23 
 0
 1
 0
 0
 5:06 
 0
 0
 0
 0
 15:57 
 0
 0
 0
 0
 9:54