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ADA-AT/DT: An Adversarial Approach for Cross-Domain and Cross-Task Knowledge Transfer
We deal with the problem of cross-task and cross-domain knowledge transfer in the realm of scene understanding for autonomous vehicles. We consider the scenario where supervision is available for a pair of tasks in a source domain while it is available for only one of the tasks in the target domain. Given that, the goal is to perform inference for the task in the target which is devoid of any training information. We argue that the only reported work in learning across tasks and domains (AT/DT)  faces the problem of domain shift between the source and target domains, hindering predictions on the target domain when the transfer of knowledge is learned on a statistically different yet related source domain. As a remedy, we develop a novel framework called ADA-AT/DT based on the adversarial training strategy to ensure that the domain-gaps are minimized for the common cross-domain supervised task. This, in effect, helps in realizing a domain-independent task-transfer function that eventually helps in performing improved inference in the target domain. We demonstrate that our proposed method significantly outperforms  by using models with 81% fewer trainable parameters. In addition, we perform experiments on a transformation mapping similar to U-Net to ensure maximum exploitation of features for task transfer. Extensive experiments have been performed on four different domains (Synthia, CityScapes, Carla, and KITTI) for two visual tasks (depth estimation and semantic segmentation) to confirm the superiority of our method.