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 data for training. Active learning techniques help us 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 available 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 called Top-Two-Margin which is given by Triplet Network. The proposed algorithm shows improved performance compared to other active learning approaches.