Title: A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction,
Abstract: The inception network has been shown to provide good performance on image
classification problems, but there are not much evidences that it is also
effective for the image restoration or pixel-wise labeling problems. For image
restoration problems, the pooling is generally not used because the decimated
features are not helpful for the reconstruction of an image as the output.
Moreover, most deep learning architectures for the restoration problems do not
use dense prediction that need lots of training parameters. From these
observations, for enjoying the performance of inception-like structure on the
image based problems we propose a new convolutional network-in-network
structure. The proposed network can be considered a modification of inception
structure where pool projection and pooling layer are removed for maintaining
the entire feature map size, and a larger kernel filter is added instead.
Proposed network greatly reduces the number of parameters on account of removed
dense prediction and pooling, which is an advantage, but may also reduce the
receptive field in each layer. Hence, we add a larger kernel than the original
inception structure for not increasing the depth of layers. The proposed
structure is applied to typical image-to-image learning problems, i.e., the
problems where the size of input and output are same such as skin detection,
semantic segmentation, and compression artifacts reduction. Extensive
experiments show that the proposed network brings comparable or better results
than the state-of-the-art convolutional neural networks for these problems.