Review: Big Data Platform Technology Stack (ps: Click to view), today we will talk about TensorFlow! Source: Zhihu, Author: He Zhiyuan During the learning process of TensorFlow, many friends have reflected that reading data is quite difficult to understand. Indeed, the official tutorial on this is relatively brief, and there aren’t many suitable learning materials … Read more
Using data augmentation techniques can increase the diversity of images in the dataset, thereby improving the performance and generalization ability of the model. The main image augmentation techniques include: Resizing Grayscale Transformation Normalization Random Rotation Center Cropping Random Cropping Gaussian Blur Brightness and Contrast Adjustment Horizontal Flip Vertical Flip Gaussian Noise Random Blocks Central Region … Read more
Introduction Graph Learning is a machine learning method that studies and applies graph-structured data. In graph learning, data is represented as a graph consisting of nodes and edges, where nodes represent entities or objects, and edges represent the relationships or connections between them. Therefore, graph learning is particularly suitable for multi-scale analysis, modeling, and simulation … Read more
We have discussed computer vision (or more narrowly, machine vision) before. Last year, the cover story of Electronic Engineering Magazine also talked about computer vision, but the content at that time was more focused on how computers acquire and understand image information from the outside world, leaning towards the perception aspect. According to the definition … Read more
Click on the above “Beginner Learns Vision“, choose to add “Star Mark” or “Top“ Essential Insights Delivered Promptly Source | Intelligent Manufacturing Bureau Introduction Defect detection is a crucial aspect of the industrial production process, and the quality of its detection results directly affects product quality. In real-world scenarios, when the defect rate is very … Read more
Development Series of GANs: PGGAN and SinGAN In the previous article, we introduced the basics of GANs (Generative Adversarial Networks) and some series of GANs. In the following series, we will continue to introduce some classic GANs. Introduction to GANs Development Series of GANs: CGAN, DCGAN, WGAN, WGAN-GP, LSGAN, BEGAN 1. PGGAN: Progressive Growing GAN … Read more
1. Introduction to GAN “Foodie, food spirit, foodies are the best of people”. This GAN foodie is not the same as that foodie. The GAN we are going to discuss is the Generative Adversarial Network proposed by Goodfellow in 2014. So what is so magical about GAN? Conventional deep learning tasks such as image classification, … Read more
The “Outcome Overview” series of articles aims to disseminate important results from conferences and journals in the field of image graphics, allowing readers to quickly understand relevant academic dynamics in their native language through short articles. We welcome your attention and submissions~ ◆ ◆ ◆ ◆ GAN Review: Models and Medical Image Fusion Applications Zhou … Read more
Table of Contents What is GAN? What Can GAN Do? Framework and Training of GAN Similarities and Differences Between GAN and Other Generative Models Existing Issues with GAN Models Introduction: GAN has gained significant popularity in the field of images over the past year, and there is a recent trend of it making inroads into … Read more
Think Tank Highlights #Global Defense Dynamics #US Military Dynamics #Russian Military Dynamics #Taiwan Affairs #WeChat Store Available #South Korea #Raytheon #Japan #Electronic Warfare #Northeast Asia Military Dynamics #Unmanned Development of Generative Adversarial Networks (GAN) Author: Military Eagle Think Tank Source: Military Eagle Dynamics Generative Adversarial Networks (GAN） is a deep learning generative model proposed by … Read more