# Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back

## TL;DR

Pseudo-task augmentation enhances single-task deep learning by simulating related tasks through multiple decoders, leading to improved performance and state-of-the-art results when combined with multitask learning.

## Contribution

The paper introduces pseudo-task augmentation, a novel method that applies multitask learning ideas to single-task settings using multiple decoders, boosting performance.

## Key findings

- Improves single-task learning performance.
- Achieves state-of-the-art on CelebA dataset.
- Complementary to traditional multitask learning.

## Abstract

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.