Image segmentation makes it easier to work with computer vision applications. Here we look at U-Net, a convolutional neural network designed for biomedical applications.
This blogpost is an in-depth discussion of the Google Brain paper titled "Searching for activation functions" which has since revived research into activation functions.
This article provides an in-depth look at the CVPR 2018 paper titled "xUnit: Learning a Spatial Activation Function for Efficient Image Restoration" and its importance in the domain of image reconstruction.
This article covers a deeper level understanding of Question Answering models in NLP, the datasets commonly used, and how to choose a pre-trained model by considering various factors like the document structure, runtime cost, etc.
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."
