Boosting Meta-Training with Base Class Information for Few-Shot Learning

arXiv:2403.03472v1 Announce Type: new
Abstract: Few-shot learning, a challenging task in machine learning, aims to learn a classifier adaptable to recognize new, unseen classes with limited labeled examples. Meta-learning has emerged as a prominent framework for few-shot learning. Its training framework is originally a task-level learning method, such as Model-Agnostic Meta-Learning (MAML) and Prototypical Networks. And a recently proposed training paradigm called Meta-Baseline, which consists of sequential pre-training and meta-training stages, gains state-of-the-art performance. However, as a non-end-to-end training method, …

abstract arxiv boosting class classifier cs.cv cs.lg examples few-shot few-shot learning framework information learn machine machine learning meta meta-learning model-agnostic training type