Learning from Imperfect Data: Incremental Learning and Few-shot Learning

Abstract In recent years, artificial intelligence (AI) has achieved great success in many fields. Although impressive advances have been made, AI algorithms still suffer from an important limitation: they rely on static and large-scale datasets. In contrast, human beings naturally possess the ability to learn novel knowledge from real-world imperfect data, such as a small number of samples or a non-static continual data stream. Attaining such an ability is particularly appealing and will push the AI models one step further toward human-level Intelligence. In this talk, I will present my work on addressing these challenges in the context of incremental learning and few-shot learning. Specifically, I will first discuss how to get better exemplars for incremental learning based on optimization. I parameterize exemplars and optimize them in an end-to-end manner to obtain high-quality, memory-efficient exemplars. Then, I will present my work on how to apply incremental learning techniques to a more challenging and realistic scenario, e.g., object detection and medical imaging. Lastly, I will briefly mention my work on addressing other challenges and discuss future research directions.