Shape Adaptor: A Learnable Resizing Module

Deep neural networks have become popular for many machine learning applications, since they provide simple strategies for end-to-end learning of complex representations. However, success can be highly sensitive to network architectures, which places a great demand on manual engineering of architectures and hyper-parameter tuning. A typical human-designed convolutional neural architecture is composed of two types of computational modules: i) a normal layer, such as a stride-1 convolution or an identity mapping, which maintains the spatial dimension of incoming feature maps; ii) a resizing layer, such as max/average pooling, bilinear sampling, or stride-2 convolution, which reshapes the incoming feature map into a different spatial dimension. We hereby define the shape of a neural network as the composition of the feature dimensions in all network layers, and the architecture as the overall structure formed by stacking multiple normal and resizing layers.