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The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. [1] [2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. [3]
The fourth generation of Deep Learning Super Sampling (DLSS) was unveiled alongside the GeForce RTX 50 series. DLSS 4 upscaling uses a new vision transformer -based model for enhanced image quality with reduced ghosting and greater image stability in motion compared to the previous convolutional neural network (CNN) model. [ 30 ]
General scheme of content-based image retrieval. Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey [1] for a scientific overview of the CBIR field).
This method uses machine learning for more detailed images, such as photographs and complex artwork. Programs that use this method include waifu2x , Imglarger and Neural Enhance. Demonstration of conventional vs. waifu2x upscaling with noise reduction, using a detail of Phosphorus and Hesperus by Evelyn De Morgan .
Sample images from MNIST test dataset. The MNIST database (Modified National Institute of Standards and Technology database [1]) is a large database of handwritten digits that is commonly used for training various image processing systems. [2] [3] The database is also widely used for training and testing in the field of machine learning.
Generative adversarial network – Deep learning method; Generative pre-trained transformer – Type of large language model; Large language model – Type of machine learning model; Music and artificial intelligence – Usage of artificial intelligence to generate music; Generative AI pornography – Explicit material produced by generative AI
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
The ImageNet training set contained 1.2 million images. The model was trained for 90 epochs over a period of five to six days using two Nvidia GTX 580 GPUs (3GB each). [ 1 ] These GPUs have a theoretical performance of 1.581 TFLOPS in float32 and were priced at US$500 upon release. [ 3 ]