SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification
This work addresses the challenge of improving classification accuracy with limited labeled data, which is incremental by building on existing consistency-based methods in semi-supervised learning.
The paper tackles the problem of semi-supervised classification by proposing a novel unsupervised objective that exploits relationships between similar high-confidence unlabeled data, resulting in significant performance gains on datasets like CIFAR-100 and Mini-ImageNet, and competitive results on CIFAR-10 and SVHN.
A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels. The goal of semi-supervised training, in this context, is to improve classification accuracy by leverage information not only from labeled data but also from a large amount of unlabeled data. Recent works have developed significant improvements by exploring the consistency constrain between differently augmented labeled and unlabeled data. Following this path, we propose a novel unsupervised objective that focuses on the less studied relationship between the high confidence unlabeled data that are similar to each other. The new proposed Pair Loss minimizes the statistical distance between high confidence pseudo labels with similarity above a certain threshold. Combining the Pair Loss with the techniques developed by the MixMatch family, our proposed SimPLE algorithm shows significant performance gains over previous algorithms on CIFAR-100 and Mini-ImageNet, and is on par with the state-of-the-art methods on CIFAR-10 and SVHN. Furthermore, SimPLE also outperforms the state-of-the-art methods in the transfer learning setting, where models are initialized by the weights pre-trained on ImageNet or DomainNet-Real. The code is available at github.com/zijian-hu/SimPLE.