Active learning using Generative Adversarial Networks for improving generalization and avoiding distractor points

Author:

Lim HeechulORCID,Chon Kang-WookORCID,Kim Min-SooORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference42 articles.

1. Learning to learn by self-critique;Antoniou,2019

2. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein Generative Adversarial Networks. In Proceedings of the 34th International conference on machine learning.

3. Ash, J. T., Zhang, C., Krishnamurthy, A., Langford, J., & Agarwal, A. (2020). Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. In 8th International conference on learning representations, ICLR.

4. Casanova, A., Pinheiro, P. O., Rostamzadeh, N., & Pal, C. J. (2020). Reinforced active learning for image segmentation. In 8th International conference on learning representations, ICLR.

5. Multi-task attention-based semi-supervised learning for medical image segmentation;Chen,2019

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