In this article, we will take a look at the paper "Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks" which serves as a design update for the popular Squeeze-and-Excitation attention module.
This article gives an in-depth summary of the ICASSP paper titled "SA-Net: Shuffle Attention for Deep Convolutional Neural Networks."
In this post, we will cover a novel form of channel attention called the Style Recalibration Module (SRM), an extension of the popular TPAMI paper: Squeeze-and-Excitation Networks.
This series gives an advanced guide to different recurrent neural networks (RNNs). You will gain an understanding of the networks themselves, their architectures, their applications, and how to bring the models to life using Keras.
In this article, we'll discuss pruning neural networks: what it is, how it works, different pruning methods, and how to evaluate them.
In this post, we will discuss a form of attention mechanism in computer vision known as Global Context Networks, first published at ICCV Workshops 2019.
In this tutorial, we'll discuss a new form of attention mechanism in computer vision known as Triplet Attention, which was accepted to WACV 2021.
