Daphne and Lukas discuss the remarkable applications of transfer learning in machine learning, from training Resnet models on web images for transfer to microscopy images to pre-training graph neural network models for chemical structures. They highlight the significance of leveraging larger datasets with less supervision to build useful models on smaller datasets in the future.
