The paper's research focused solely on single images, but the team at Apple notes towards the end that it hopes to sometime soon "investigate refining videos" as well.

This essay on the lessons we learned about deep learning systems and gender recognition is one part of a three-part examination of issues relating to machine vision technology.

Thanks to machine learning techniques using neural networks and deep learning, that's becoming more achievable.

Apple's machine learning researchers have worked on myriad ways to improve Apple Intelligence and other generative AI systems, as its research papers accepted by a major AI conference demonstrate.

These words are straight from a review I received for a paper I submitted to the NeurIPS (Neural Information Processing Systems) conference, a top venue for machine-learning research.

But neural network-based image recognition algorithms are still far from perfect, and according to a pair of recent papers these algorithms can be tricked pretty easily.

AI, Machine Learning & Robotics research at Drexel University's College of Computing & Informatics (CCI) explores algorithms, mathematics, and applications of artificial intelligence (AI) through ...

Through innovative use of a neural network that mimics image processing by the human brain, a research team reports accurate reconstruction of images transmitted over optical fibers for distances ...

Dr. James McCaffrey of Microsoft Research demonstrates how to fetch and prepare MNIST data for image recognition machine learning problems.