Abstract
Convolutional neural networks are witnessing wide adoption in computer vision systems with numerous applications
across a range of visual recognition tasks. Much of this
progress is fueled through advances in convolutional neural network architectures and learning algorithms even as
the basic premise of a convolutional layer has remained unchanged. In this paper, we seek to revisit the convolutional
layer that has been the workhorse of state-of-the-art visual
recognition models. We introduce a very simple, yet effective,
module called a perturbation layer as an alternative to a
convolutional layer. The perturbation layer does away with
convolution in the traditional sense and instead computes its
response as a weighted linear combination of non-linearly
activated additive noise perturbed inputs. We demonstrate
both analytically and empirically that this perturbation layer
can be an effective replacement for a standard convolutional
layer. Empirically, deep neural networks with perturbation layers, called Perturbative Neural Networks (PNNs),
in lieu of convolutional layers perform comparably with standard CNNs on a range of visual datasets (MNIST, CIFAR-10,
PASCAL VOC, and ImageNet) with fewer parameters