CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining

Non-stationary data stream mining aims to classify large scale online instances that emerge continuously. The most apparent challenge compared with the offline learning manner is the issue of consecutive emergence of new categories, when tackling non-static categorical distribution. Non-stationary stream settings often appear in real-world applications, e.g., online classification in E-commerce systems that involves the incoming productions, or the summary of news topics on social networks (Twitter). Ideally, a learning model should be able to learn novel concepts from labeled data (in new tasks) and reduce the abrupt degradation of model performance on the old concept (also named catastrophic forgetting problem). In this work, we focus on improving the performance of the stream mining approach under the constrained resources, where both the memory resource of old data and labeled new instances are limited/scarce. We propose a simple yet efficient resource-constrained framework CLEAR to facilitate previous challenges during the one-pass stream mining. Specifically, CLEAR focuses on creating and calibrating the class representation (the prototype) in the embedding space. We first apply the contrastive-prototype learning on large amount of unlabeled data, and generate the discriminative prototype for each class in the embedding space. Next, for updating on new tasks/categories, we propose a drift estimation strategy to calibrate/compensate for the drift of each class representation, which could reduce the knowledge forgetting without storing any previous data. We perform experiments on public datasets (e.g., CUB200, CIFAR100) under stream setting, our approach is consistently and clearly better than many state-of-the-art methods, along with both the memory and annotation restriction.