methodologies Computer vision&lt;/concept\_desc&gt; &lt;concept\_significance&gt;100&lt;/concept\_significance&gt; &lt;/concept&gt; &lt;/ccs2012&gt;

Introduction
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In multitask learning (MTL) , a neural network is trained simultaneously to perform several different tasks at once [@Caruana:1998]. For instance, given an image as input, it can recognize the objects in it, identify the type of scene, and generate a verbal caption for it. Typically the early parts of the network are shared between tasks, and the later parts, leading to the different tasks, are separate [@Caruana:1998; @Collobert:2008; @Dong:2015; @Lu:2016; @Ranjan:2016]. The network is trained with gradient descent in all these tasks, and therefore the requirements of all tasks are combined in the shared parts of the network. The embeddings thus reflect the requirements of all tasks, making them more robust and general. Performance of a multitask network in each task can therefore exceed the performance of a network trained in only a single task.

Much of the research in deep learning in recent years has focused on coming up with better architectures, and MTL is no exception. As a matter of fact, architecture plays possibly an even larger role in MTL because there are many ways to tie the multiple tasks together. The best network architectures are large and complex, 