Few-shot learning with sequence-processing neural networks (NNs) has recently
attracted a new wave of attention in the context of large language models. In
the standard N-way K-shot learning setting, an NN is explicitly optimised to
learn to classify unlabelled inputs by observing a sequence of NK labelled
examples. This pressures the NN to learn a learning algorithm that achieves
optimal performance, given the limited number of training examples. Here we
study an auxiliary loss that encourages further acceleration of few-shot
learning, by applying recently proposed bootstrapped meta-learning to NN
few-shot learners: we optimise the K-shot learner to match its own performance
achievable by observing more than NK examples, using only NK examples.
Promising results are obtained on the standard Mini-ImageNet dataset. Our code
is public.

研究利用 bootstrapped meta-learning 提高少样本学习加速的辅助损失方法，应用在神经网络中，取得了 Mini-ImageNet 数据集上的良好结果，且代码公开。

通过引导加速神经自我改进

Accelerating Neural Self-Improvement via Bootstrapping

Few-shot learning is the process of learning novel classes using only a few
examples and it remains a challenging task in machine learning. Many
sophisticated few-shot learning algorithms have been proposed based on the
notion that networks can easily overfit to novel examples if they are simply
fine-tuned using only a few examples. In this study, we show that in the
commonly used low-resolution mini-ImageNet dataset, the fine-tuning method
achieves higher accuracy than common few-shot learning algorithms in the 1-shot
task and nearly the same accuracy as that of the state-of-the-art algorithm in
the 5-shot task. We then evaluate our method with more practical tasks, namely
the high-resolution single-domain and cross-domain tasks. With both tasks, we
show that our method achieves higher accuracy than common few-shot learning
algorithms. We further analyze the experimental results and show that: 1) the
retraining process can be stabilized by employing a low learning rate, 2) using
adaptive gradient optimizers during fine-tuning can increase test accuracy, and
3) test accuracy can be improved by updating the entire network when a large
domain-shift exists between base and novel classes.

该研究证明，在 mini-ImageNet 数据集中，fine-tuning 方法在 1-shot 任务中比常见的 few-shot learning 算法具有更高的准确性，在 5-shot 任务中准确率几乎与最先进的算法相同，同时展示了在高分辨率单领域和跨领域任务中，该方法比常见 few-shot learning 算法具有更高的准确性。