论文标题
在卷积网络中分类用于一次性学习
Sorted Pooling in Convolutional Networks for One-shot Learning
论文作者
论文摘要
我们介绍了常用的最大池操作的广义版本：$ k $ th和排序的池操作，这些操作选择了每个池区$ k $第一个最大响应，从而选择了输入图像的本地一致特征。该方法能够提高网络的概括能力，可用于降低网络的训练时间和错误率，并且在培训场景的情况下，可以显着提高准确性，而可用数据的数量有限，例如一次性学习方案
We present generalized versions of the commonly used maximum pooling operation: $k$th maximum and sorted pooling operations which selects the $k$th largest response in each pooling region, selecting locally consistent features of the input images. This method is able to increase the generalization power of a network and can be used to decrease training time and error rate of networks and it can significantly improve accuracy in case of training scenarios where the amount of available data is limited, like one-shot learning scenarios
