  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, indicating the
meta-training stage can only begin after the completion of pre-training,
Meta-Baseline suffers from higher training cost and suboptimal performance due
to the inherent conflicts of the two training stages. To address these
limitations, we propose an end-to-end training paradigm consisting of two
alternative loops. In the outer loop, we calculate cross entropy loss on the
entire training set while updating only the final linear layer. In the inner
loop, we employ the original meta-learning training mode to calculate the loss
and incorporate gradients from the outer loss to guide the parameter updates.
This training paradigm not only converges quickly but also outperforms existing
baselines, indicating that information from the overall training set and the
meta-learning training paradigm could mutually reinforce one another. Moreover,
being model-agnostic, our framework achieves significant performance gains,
surpassing the baseline systems by approximate 1%.
