  Deep multitask learning boosts performance by sharing learned structure
across related tasks. This paper adapts ideas from deep multitask learning to
the setting where only a single task is available. The method is formalized as
pseudo-task augmentation, in which models are trained with multiple decoders
for each task. Pseudo-tasks simulate the effect of training towards
closely-related tasks drawn from the same universe. In a suite of experiments,
pseudo-task augmentation is shown to improve performance on single-task
learning problems. When combined with multitask learning, further improvements
are achieved, including state-of-the-art performance on the CelebA dataset,
showing that pseudo-task augmentation and multitask learning have complementary
value. All in all, pseudo-task augmentation is a broadly applicable and
efficient way to boost performance in deep learning systems.
