使用预训练和镜头的少样本学习，通过预训练样本进行丰富
Few-shot learning using pre-training and shots, enriched by pre-trained samples
摘要：
我们使用EMNIST手写数字数据集来测试一种简单的少样本学习方法。完全连接的神经网络用10个数字的子集进行预训练，并用于用未训练的数字进行少样本学习。引入了两个基本思想：在少样本学习期间，第一层的学习被禁用，并且对于每个镜头，先前未知的数字与四个先前训练的数字一起用于梯度下降，直到满足预定义的阈值条件。这样，我们在10次射击后达到约90%的准确率。
英文摘要：
We use the EMNIST dataset of handwritten digits to test a simple approach for few-shot learning. A fully connected neural network is pre-trained with a subset of the 10 digits and used for few-shot learning with untrained digits. Two basic ideas are introduced: during few-shot learning the learning of the first layer is disabled, and for every shot a previously unknown digit is used together with four previously trained digits for the gradient descend, until a predefined threshold condition is fulfilled. This way we reach about 90% accuracy after 10 shots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
