The field of computer vision was revolutionized in 2012 with the advent of deep learning. We’re in the year 2021 and the latest results look poised to shake up the field again : Non-convolutional architectures produce state of the art results, and they leverage of large amounts of weakly labelled data leads to impressive results on data never seen during training. This talk will start from the very basics using ML for image classification, dive into transfer learning, and finally focus on recent research results, such as vision transformers and contrastive learning from text and images.
Andreas is a software engineer at Google Zurich. He holds a medical degree from Université de Lausanne and a masters in bioelectronics from ETH Zurich. He’s worked as a civil servant in Tanzania and as a Doctoral Student in the Swiss Tropical and Public Health Institute. He’s been in his current position at Google for 6 years.