Meta-learning and application on few-shot learning

Tóm tắt báo cáo

In recent years, high-capacity models, such as deep neural networks, have enabled very powerful machine learning techniques in domains where data is plentiful. However, domains where data is scarce have proven challenging for such methods because high-capacity function approximators critically rely on large datasets for generalization. This can pose a major challenge for domains ranging from supervised medical image processing to reinforcement learning where real-world data collection (e.g., for robots) poses a major logistical challenge. Meta-learning or few-shot learning offers a potential solution to this problem: by learning to learn across data from many previous tasks, few-shot meta-learning algorithms can discover the structure among tasks to enable fast learning of new tasks.