Siamese network representation for active learning

Active learning is a crucial part of machine learning aiming to reduce the amount of labeled data by selecting the most informative data to be annotated. Most of the previous proposed active learning methods are based on aleatoric or epistemic uncertainties obtained by learning models while ignoring relationships within the data itself. We propose an efficient similarity-based active learning method using siamese convolutional neural networks. Pairs of image data are sent into the siamese network and similarity between them is computed on their output features. We evaluate our method on image classification, and validate the method on CIFAR10/100 and Caltech101 dataset. Our method outperforms at most 3.17% accuracy than Bayesian-based method and 6.31% than random sample. In addition, we propose a hierarchical clustering method for pool-based sampling strategies, which will boost the representation stage of our method. We also conduct an ablation study to fully explore the efficiency of our method.