Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance
This addresses the challenge of reducing labeling effort for deep learning applications, though it is incremental as it builds on existing semi-supervised techniques.
The paper tackles the problem of achieving fully supervised learning performance using only one labeled sample per class and unlabeled data, demonstrating on CIFAR-10 and SVHN that their method attains test accuracies comparable to fully supervised learning.
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 the first time the potential for building one-shot semi-supervised (BOSS) learning on Cifar-10 and SVHN up to attain test accuracies that are comparable to fully supervised learning. Our method combines class prototype refining, class balancing, and self-training. A good prototype choice is essential and we propose a technique for obtaining iconic examples. In addition, we demonstrate that class balancing methods substantially improve accuracy results in semi-supervised learning to levels that allow self-training to reach the level of fully supervised learning performance. Rigorous empirical evaluations provide evidence that labeling large datasets is not necessary for training deep neural networks. We made our code available at https://github.com/lnsmith54/BOSS to facilitate replication and for use with future real-world applications.