Meta AI Releases Data2vec v2.0: A Self-Supervised Learning Tool for Machine Learning Tasks
Self-supervised Learning is a type of unsupervised education in which the learning task is built from unlabeled raw data. Supervised learning can be effective, but requires a lot of labeled information. It is difficult to get high-quality data, and it takes a lot of resources, especially when you are working on more complex tasks, such as object detection or instance segmentation.
Self-supervised learning is a method of self-supervised learning that aims to learn useful representations from unlabeled data and refine them with fewer labels to perform downstream tasks, such as image classification or semantic segmentation.
Many recent advancements in artificial intelligence are based on self-supervised learning. Existing algorithms are geared towards a specific modality (such images or text), and they require a lot of computer resources. Humans are able to learn much more quickly than AI, and they can learn from a variety of sources.