Metric Based Few Shot

Metric-based few-shot learning aims to train models that can classify new objects or events with only a handful of examples, addressing the challenge of limited data in many real-world applications. Current research focuses on improving the learned embedding spaces by developing novel loss functions that enhance class separability and adapting embedding functions to unseen data through online learning techniques, often leveraging meta-learning approaches. These advancements are improving the robustness and accuracy of few-shot classifiers, particularly in addressing issues like shot sensitivity and domain adaptation, leading to more effective models for image and sound event recognition.