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Partial Transfer Learning with Selective Adversarial Networks | IEEE Conference Publication | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. This paper introduces partial transfer learning, which relaxes the shared label space assumption to that the target label space is only a subspace of the source label space.
2. Selective Adversarial Network (SAN) is proposed to simultaneously circumvent negative transfer by selecting out the outlier source classes and promote positive transfer by maximally matching the data distributions in the shared label space.
3. Experiments demonstrate that SAN models exceed state-of-the-art results for partial transfer learning tasks on several benchmark datasets.
Article analysis:
The article provides an overview of a new approach to partial transfer learning, introducing Selective Adversarial Networks (SAN). The article presents a clear explanation of the concept and its potential applications, as well as providing evidence from experiments demonstrating its effectiveness on several benchmark datasets. The article does not appear to be biased or one-sided, presenting both sides of the argument fairly and objectively. It also does not appear to contain any unsupported claims or missing points of consideration, as all claims are backed up with evidence from experiments. Furthermore, there are no promotional content or partiality present in the article, as it is purely focused on presenting research findings and conclusions without any bias towards any particular product or service. Finally, possible risks associated with this approach are noted in the article, such as negative transfer due to outlier source classes. In conclusion, this article appears to be trustworthy and reliable in terms of its content and presentation.