Active Learning through Adversarial Exploration in Contrastive Objectives
Learning by contrasting positive and negative samples is a general strategy adopted by many methods in cases where the original formulation has intractable computational requirements or is difficult to model. Noise Contrastive Estimation (NCE) and negative sampling in word embeddings are examples that make use of this contrastive learning strategy. In this paper, we take an active learning perspective on negative mining whereby negative examples are chosen via a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn better representations of the data. We empirically show on a toy task that negative examples mined through our strategy explores regions near the decision boundaries in the embedded space a behavior that is different from other active learning strategies. We give a characterization between the connection of selected active learning strategies and recent work in intrinsic motivation in Deep Reinforcement Learning and visual semantic embeddings. We evaluate our proposal on learning word embeddings and observe both faster convergence and improved results on multiple metrics.