  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.
