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cs.LG2022
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning
Yeachan Kim, Bonggun Shin
Active learning enables the efficient construction of a labeled dataset by labeling informative samples from an unlabeled dataset. In a real-world active learning scenario, conside…
cs.CL2022
Context-based Virtual Adversarial Training for Text Classification with Noisy Labels
Do-Myoung Lee, Yeachan Kim, Chang-gyun Seo
Deep neural networks (DNNs) have a high capacity to completely memorize noisy labels given sufficient training time, and its memorization, unfortunately, leads to performance degra…