  Semi-supervised learning (SSL) is an efficient framework that can train
models with both labeled and unlabeled data, but may generate ambiguous and
non-distinguishable representations when lacking adequate labeled samples. With
human-in-the-loop, active learning can iteratively select informative unlabeled
samples for labeling and training to improve the performance in the SSL
framework. However, most existing active learning approaches rely on
pre-trained features, which is not suitable for end-to-end learning. To deal
with the drawbacks of SSL, in this paper, we propose a novel end-to-end
representation learning method, namely ActiveMatch, which combines SSL with
contrastive learning and active learning to fully leverage the limited labels.
Starting from a small amount of labeled data with unsupervised contrastive
learning as a warm-up, ActiveMatch then combines SSL and supervised contrastive
learning, and actively selects the most representative samples for labeling
during the training, resulting in better representations towards the
classification. Compared with MixMatch and FixMatch with the same amount of
labeled data, we show that ActiveMatch achieves the state-of-the-art
performance, with 89.24% accuracy on CIFAR-10 with 100 collected labels, and
92.20% accuracy with 200 collected labels.
