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.
In general, Azure notebooks are best for those who'd like to take advantage of starter credits from Microsoft or for those who are already entrenched in the Azure computing ecosystem while Gradient is best for running Free CPU and GPU instances without a lot of startup time or hassle.
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.
