  Humans and animals have a natural ability to quickly adapt to their
surroundings, but machine-learning models, when subjected to changes, often
require a complete retraining from scratch. We present Knowledge-adaptation
priors (K-priors) to reduce the cost of retraining by enabling quick and
accurate adaptation for a wide-variety of tasks and models. This is made
possible by a combination of weight and function-space priors to reconstruct
the gradients of the past, which recovers and generalizes many existing, but
seemingly-unrelated, adaptation strategies. Training with simple first-order
gradient methods can often recover the exact retrained model to an arbitrary
accuracy by choosing a sufficiently large memory of the past data. Empirical
results show that adaptation with K-priors achieves performance similar to full
retraining, but only requires training on a handful of past examples.
