Machine learning practitioners are increasingly turning to the power of generative adversarial networks (GANs) for image processing. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many others.
The field of digital image processing is increasingly shifting from deterministic mathematical calculations and statistical methods, to machine learning (ML)-based approaches which can provide better and more accurate results. This in turn is also helping to drive new methods and use cases for computer vision in the pursuit of
Ah 2020! From global healthcare issues to revolutions in how technology is being adopted and even repurposed, it has been quite a year. At the end of each year, it's always fun to pause and think about machine learning (ML) trends which have seen phenomenal growth, especially around tools, resources,