Proposes a new set of ImageNet labels that address the limitations of the original labels resulting from multiple objects in a single image and synonymous labels.
BYOL improves on SotA self-supervised methods by introducing a target network, which removes the need for negative examples.
Investigates automatically generating curricula based on a variety of progress signals that are computed for each data sample.