Calum Heggan

Amortised Invariance Learning for Contrastive Self-Supervision

Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations. Invariances established during pre-training can be interpreted as strong inductive biases. However these may or may not be helpful, dependi…