In convolutional neural network-based character recognition, pooling layers play an important role in dimensionality reduction and deformation compensation. However, their kernel shapes and pooling operations are empirically predetermined; typically, a fixed-size square kernel shape and max pooling operation are used. In this paper, we propose a meta-learning framework for pooling layers. As part of our framework, a parameterized pooling layer is proposed in which the kernel shape and pooling operation are trainable using two parameters, thereby allowing flexible pooling of the input data. We also propose a meta-learning algorithm for the parameterized pooling layer, which allows us to acquire a suitable pooling layer across multiple tasks. In the experiment, we applied the proposed meta-learning framework to character recognition tasks. The results demonstrate that a pooling layer that is suitable across character recognition tasks was obtained via meta-learning, and the obtained pooling layer improved the performance of the model in both few-shot character recognition and noisy image recognition tasks.

翻译：在共进神经网络特征识别中,集合层在维度减少和变形补偿方面起着重要作用。然而,它们的内核形状和集合作业是经经验决定的;通常使用固定大小的正方内核形状和最大集合作业。在本文件中,我们提议了一个集合层的元学习框架。作为我们框架的一部分,提议了一个参数化集合层,利用两个参数对内核形状和集合作业进行训练,从而允许灵活地汇集输入数据。我们还提议了一个参数化集合层的元学习算法,使我们能够在多种任务之间获得一个合适的集合层。在试验中,我们将拟议的元学习框架用于识别特征任务。结果显示,一个适合跨特性识别任务的集合层是通过元学习获得的,而获得的集合层改进了该模型在微小特征识别和噪音图像识别任务方面的性能。