822 citations · 899 across the 14 of their papers we have counts for
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cs.LG2020
FROST: Faster and more Robust One-shot Semi-supervised Training
Helena E. Liu, Leslie N. Smith
Recent advances in one-shot semi-supervised learning have lowered the barrier for deep learning of new applications. However, the state-of-the-art for semi-supervised learning is s…
cs.LG2020★ 2 cited
Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance
Leslie N. Smith, Adam Conovaloff
Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications. We demonstrate for t…
cs.LG2020★ 1 cited
Empirical Perspectives on One-Shot Semi-supervised Learning
Leslie N. Smith, Adam Conovaloff
One of the greatest obstacles in the adoption of deep neural networks for new applications is that training the network typically requires a large number of manually labeled traini…