Abstract: Domain randomization is a data synthesis approach for training neural network models on synthetic images for the purpose of generalization to real-world images through randomizing parameters ...

Abstract. Convolutional neural networks are widely used to solve the problem of object recognition in images and have many advantages over classical image processing algorithms. However, training such ...

"WireColor Pro" is a powerful open source script designed for Autodesk 3ds Max that enhances the workflow of 3D designers and artists by providing advanced tools for manipulating object colors, ...

Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, ...

In conclusion, we found that domain randomization is a viable strategy for the detection of industrial objects. However, domain knowledge can be used for object-related aspects to improve detection ...

Before delving into debugging, it is critical to have a solid understanding of the basics of SystemVerilog constraint randomization. Constraints are used to define the valid range of values for ...

A recent review of randomization methods used in large multicenter clinical trials within the National Institutes of Health Stroke Trials Network identified preservation of treatment allocation ...