Adversarial Transfer Learning
By Garrett Wilson, Diane Joyce Cook. 2018
14 citations recorded by the source corpus.
There is a recent large and growing interest in generative adversarial networks (GANs), which offer powerful features for generative modeling, density estimation, and energy function learning. GANs are difficult to train and evaluate but are capable of creating amazingly realistic, though synthetic, image data. Ideas stemming from GANs such as adversarial losses are creating research opportunities for other challenges such as domain adaptation. In this paper, we look at the field of GANs with emphasis on these areas of emerging research. To provide background for adversarial techniques, we survey the field of GANs, looking at the original formulation, training variants, evaluation methods, and extensions. Then we survey recent work on transfer learning, focusing on comparing different adversarial domain adaptation methods. Finally, we take a look forward to identify open research directions for GANs and domain adaptation, including some promising applications such as sensor-based human behavior modeling.
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- Soujanya Poria
- Navonil Majumder
- Deepanway Ghosal
- Devamanyu Hazarika
- Rada Mihalcea
- Erik Cambria
- Christian Simon
- Yi-Hsuan Tsai
Citations in the corpus
- On Generalizing Beyond Domains in Cross-Domain Continual Learning (2022, background): “in these problems the test data is accessible during training [11, 37].”
- KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis (2020, background): “Domain adaptation, a case of transductive transfer learning, is a widely studied field of research that can be effectively used to tackle this problem (Wilson and Cook, 2018).”