Title: Uncertainty in Multitask Transfer Learning,
Abstract: Using variational Bayes neural networks, we develop an algorithm capable of
accumulating knowledge into a prior from multiple different tasks. The result
is a rich and meaningful prior capable of few-shot learning on new tasks. The
posterior can go beyond the mean field approximation and yields good
uncertainty on the performed experiments. Analysis on toy tasks shows that it
can learn from significantly different tasks while finding similarities among
them. Experiments of Mini-Imagenet yields the new state of the art with 74.5%
accuracy on 5 shot learning. Finally, we provide experiments showing that other
existing methods can fail to perform well in different benchmarks.