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Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representations from multiple supervisory tasks. However, existing multi-task approaches rely on enumerating multiple network architectures specific to the tasks at hand, that do not generalize. In this paper, we propose a principled approach to learn shared representations in ConvNets using multitask learning. Specifically, wedoi:10.1109/cvpr.2016.433 dblp:conf/cvpr/MisraSGH16 fatcat:lefrg2pwzfev5kckjntlu377ve