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

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

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 …

Abstract ResNet or DenseNet? Nowadays, most deep learning based approaches are
implemented with seminal backbone networks, among them the two arguably most famous …

Recent work has shown that convolutional networks can be substantially deeper, more
accurate, and efficient to train if they contain shorter connections between layers close to the …

Deep convolutional neural network (ConvNet) is a promising approach for high-performance
image classification. The behavior of ConvNet is analyzed mainly based on the neuron …

A number of studies have shown that increasing the depth or width of convolutional
networks is a rewarding approach to improve the performance of image recognition. In our …

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 …

The fully connected layers of a deep convolutional neural network typically contain over
90% of the network parameters, and consume the majority of the memory required to store …

In computer vision, 2D convolution is arguably the most important operation performed by a
ConvNet. Unsurprisingly, it has been the focus of intense software and hardware …