  Compact neural network offers many benefits for real-world applications.
However, it is usually challenging to train the compact neural networks with
small parameter sizes and low computational costs to achieve the same or better
model performance compared to more complex and powerful architecture. This is
particularly true for multitask learning, with different tasks competing for
resources. We present a simple, efficient and effective multitask learning
overparameterisation neural network design by overparameterising the model
architecture in training and sharing the overparameterised model parameters
more effectively across tasks, for better optimisation and generalisation.
Experiments on two challenging multitask datasets (NYUv2 and COCO) demonstrate
the effectiveness of the proposed method across various convolutional networks
and parameter sizes.
