Convolutional neural networks process input data by sending channel-wise feature
response maps to subsequent layers. While a lot of progress has been made by making …

A variety of attention mechanisms have been studied to improve the performance of various
computer vision tasks. However, the prior methods overlooked the significance of retaining …

Depthwise convolution and grouped convolution has been successfully applied to improve
the efficiency of convolutional neural network (CNN). We suggest that these models can be …

Recent strategies achieved ensembling"" for free"" by fitting concurrently diverse
subnetworks inside a single base network. The main idea during training is that each …

This paper presents a new technique for training networks for low-precision communication.
Targeting minimal communication between nodes not only enables the use of emerging …

Convolutional neural networks (CNNs) have shown great capability of solving various
artificial intelligence tasks. However, the increasing model size has raised challenges in …

Recently, channel attention mechanism has demonstrated to offer great potential in
improving the performance of deep convolutional neural networks (CNNs). However, most …

Attention mechanism, especially channel attention, has gained great success in the
computer vision field. Many works focus on how to design efficient channel attention …

Channel pruning approaches for convolutional neural networks (ConvNets) deactivate the
channels statically or dynamically and require special implementation. In addition channel …

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 …