论文标题
推动简单管道的极限进行几次学习：外部数据和微调会有所作为
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
论文作者
论文摘要
在计算机视觉中，很少有射击学习（FSL）是一个重要且主题的问题，它激发了对从复杂的元学习方法到简单转移学习基线的多种方法进行广泛研究。我们试图推动简单但有效的管道的限制，以进行更现实和实用的图像分类。为此，我们从神经网络体系结构的角度探索了很少的学习学习，以及在不同的数据供应下的三阶段网络更新管道，其中考虑了无监督的外部数据用于预培训，基本类别用于模拟元训练的少量任务，以及用于征用小说任务的元数据训练的少量任务。我们研究了以下问题：（1）对外部数据的预培训如何使FSL受益？ （2）如何利用最先进的变压器体系结构？ （3）微调如何减轻域移动？最终，我们表明，简单的基于变压器的管道在标准基准测试（例如Mini-Imagenet，Cifar-FS，CDFSL和Meta-dataset）上产生了令人惊讶的良好性能。我们的代码和演示可在https://hushell.github.io/pmf上找到。
Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for more realistic and practical settings of few-shot image classification. To this end, we explore few-shot learning from the perspective of neural network architecture, as well as a three stage pipeline of network updates under different data supplies, where unsupervised external data is considered for pre-training, base categories are used to simulate few-shot tasks for meta-training, and the scarcely labelled data of an novel task is taken for fine-tuning. We investigate questions such as: (1) How pre-training on external data benefits FSL? (2) How state-of-the-art transformer architectures can be exploited? and (3) How fine-tuning mitigates domain shift? Ultimately, we show that a simple transformer-based pipeline yields surprisingly good performance on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset. Our code and demo are available at https://hushell.github.io/pmf.
