  Convolutional Neural Networks (CNNs) have shown impressive performance in
computer vision tasks such as image classification, detection, and
segmentation. Moreover, recent work in Generative Adversarial Networks (GANs)
has highlighted the importance of learning by progressively increasing the
difficulty of a learning task [26]. When learning a network from scratch, the
information propagated within the network during the earlier stages of training
can contain distortion artifacts due to noise which can be detrimental to
training. In this paper, we propose an elegant curriculum based scheme that
smoothes the feature embedding of a CNN using anti-aliasing or low-pass
filters. We propose to augment the train-ing of CNNs by controlling the amount
of high frequency information propagated within the CNNs as training
progresses, by convolving the output of a CNN feature map of each layer with a
Gaussian kernel. By decreasing the variance of the Gaussian kernel, we
gradually increase the amount of high-frequency information available within
the network for inference. As the amount of information in the feature maps
increases during training, the network is able to progressively learn better
representations of the data. Our proposed augmented training scheme
significantly improves the performance of CNNs on various vision tasks without
either adding additional trainable parameters or an auxiliary regularization
objective. The generality of our method is demonstrated through empirical
performance gains in CNN architectures across four different tasks: transfer
learning, cross-task transfer learning, and generative models.
