Abstract: Current image compression algorithms based on transforms can achieve ideal performance for natural images, but do not do well with synthetic aperture radar (SAR) images. We propose a learned ...

Abstract: In recent years, the rapid advancement of autonomous driving technology has driven the widespread development and application of LiDAR (Light Detection and Ranging). LiDAR provides highly ...

How can autonomous vehicles continuously learn new traffic scenarios without forgetting previously learned ones? Researchers ...

In this post, we will cover some of the best ways to compress images without losing quality, either a single image or in bulk, online, or using free Windows software. At times, you might need to ...

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...

This project implements a deep Convolutional Autoencoder with skip connections (U-Net style) for image denoising. It covers both Deep Learning and Image Processing curriculum requirements by combining ...

Investigators developed and validated a masked autoencoder deep learning model using vision transformer technology to automate the detection and grading of nuclear cataracts from slit-lamp images.