The DOI matches its Crossref record.
Generative Adversarial Networks
Abstract
For many AI projects, deep learning techniques are increasingly being used as the building blocks for innovative solutions ranging from image classification to object detection, image segmentation, image similarity, and text analytics (e.g., sentiment analysis, key phrase extraction). GANs, first introduced by Goodfellow et al. (2014), are emerging as a powerful new approach toward teaching computers how to do complex tasks through a generative process. As noted by Yann LeCun (at http://bit.ly/LeCunGANs ), GANs are truly the “coolest idea in machine learning in the last 20 years.” These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Jointly Measuring Diversity and Quality in Text Generation Models
Alihosseini, D., Montahaei, E., & Soleymani Baghshah, M. (2019) · Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation · doi:10.18653/v1/w19-2311 · Cited by 48
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Metadata
- Authors
- Salvaris, M., Dean, D., & Tok, W. H.
- Venue
- Deep Learning with Azure
- Publisher
- Apress
- Published
- 2018
- Pages
- 187-208
- OpenAlex
- W3148140980
- Cited by
- 375
- References
- 0
- FWCI
- 159.81
- Type
- book chapter
- Language
- en
- Open access
- closed
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