Researchers are training image-generating AI with fewer labels

Generative AI models have a propensity for learning complex data distributions, which is why they're great at producing human-like speech and convincing images of burgers and faces. But training these models requires lots of labeled data, and depending on the task at hand, the necessary corpora are sometimes in short supply. The solution might lie in an approach proposed by researchers at Google and ETH Zurich. In a paper published on the preprint server Arxiv.org These self- and semi-supervised techniques together, they say, can outperform state-of-the-art methods on popular benchmarks like ImageNet.