Inner product-based convolution has been a central component of convolutional neural
networks (CNNs) and the key to learning visual representations. Inspired by the observation …

Despite the importance of image representations such as histograms of oriented gradients
and deep Convolutional Neural Networks (CNN), our theoretical understanding of them …

Quantization is a popular way of increasing the speed and lowering the memory usage of
Convolution Neural Networks (CNNs). When labelled training data is available, network …

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs).
In this paper, we propose to augment a convolutional layer with an additional depthwise …

In this paper we present a deep neural network topology that incorporates a simple to
implement transformation-invariant pooling operator (TI-pooling). This operator is able to …

In this work, we develop a novel method for automatically learning aspects of the structure of
a deep model, in order to improve its performance, especially when labeled training data are …

Convolutional neural networks (CNNs) have shown great success in computer vision,
approaching human-level performance when trained for specific tasks via application …

We present an interpretation of Inception modules in convolutional neural networks as being
an intermediate step in-between regular convolution and the depthwise separable …

We present a deep layered architecture that generalizes convolutional neural networks
(ConvNets). The architecture, called SimNets, is driven by two operators:(i) a similarity …

Convolutional Neural Networks (CNNs) have revolutionized image classification by
extracting spatial features and enabling state-of-the-art accuracy in vision-based tasks. The …