2018

Adversarial Sampling for Active Learning

Mayer, Christoph, Timofte, Radu

Understand

This paper proposes asal, a new GAN based active learning method that generates high entropy samples.

Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training.
Hence, the quality of new samples is high and annotations are reliable.
To the best of our knowledge, ASAL is the first GAN based AL method applicable to multi-class problems that outperforms random sample selection.

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