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.

紧凑型神经网络面向多任务学习的过参数化设计以提高优化和泛化性能，通过在训练过程中对模型架构进行过参数化，并更有效地共享过参数化的模型参数，以在小参数大小和低计算成本下获得与更复杂和强大架构相同或更好的模型性能。在两个具有挑战性的多任务数据集（NYUv2 和 COCO）上的实验证明了所提方法在各种卷积网络和参数大小下的有效性。