Title: Training Group Orthogonal Neural Networks with Privileged Information,
Abstract: Learning rich and diverse representations is critical for the performance of
deep convolutional neural networks (CNNs). In this paper, we consider how to
use privileged information to promote inherent diversity of a single CNN model
such that the model can learn better representations and offer stronger
generalization ability. To this end, we propose a novel group orthogonal
convolutional neural network (GoCNN) that learns untangled representations
within each layer by exploiting provided privileged information and enhances
representation diversity effectively. We take image classification as an
example where image segmentation annotations are used as privileged information
during the training process. Experiments on two benchmark datasets -- ImageNet
and PASCAL VOC -- clearly demonstrate the strong generalization ability of our
proposed GoCNN model. On the ImageNet dataset, GoCNN improves the performance
of state-of-the-art ResNet-152 model by absolute value of 1.2% while only uses
privileged information of 10% of the training images, confirming effectiveness
of GoCNN on utilizing available privileged knowledge to train better CNNs.