Semi-Supervised Learning via Triplet Network Based Active Learning (Student Abstract).

In recent years, deep learning models have pushed state-of-the-art accuracies for several machine learning tasks. However, such models require a large amount of (supervised) data for training. While unlabelled data is available in abundance, manually labeling them is very costly. Active learning techniques helps in utilizing unlabelled data which may result in an improved classification model. In this research, we present an active learning algorithm which can help in increasing performance of deep learning models by using large amount of unlabelled data. A novel active learning algorithm, Triplet AL is proposed which uses a triplet network to select samples from an unlabelled data set. Previous active learning methods rely on classification model's final prediction scores as a measure of confidence for an unlabelled sample. We propose a more reliable confidence measure, termed as Top-Two-Margin which is given by the Triplet Network. The proposed algorithm shows improved performance compared to other active learning approaches.