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This paper takes a parallel learning approach for robust and transparent AI.A deep neural network is trained in parallel on multiple tasks, where each taskis trained only on a subset of the network resources. Each subset consists ofnetwork segments, that can be combined and shared across specific tasks. Taskscan share resources with other tasks, while having independent task-relatednetwork resources. Therefore, the trained network can share similarrepresentations across various tasks, while also enabling independenttask-related representations. The above allows for some crucial outcomes. (1)The parallel nature of our approach negates the issue of catastrophicforgetting. (2) The sharing of segments uses network resources moreefficiently. (3) We show that the network does indeed use learned knowledgefrom some tasks in other tasks, through shared representations. (4) Throughexamination of individual task-related and shared representations, the modeloffers transparency in the network and in the relationships across tasks in amulti-task setting. Evaluation of the proposed approach against complexcompeting approaches such as Continual Learning, Neural Architecture Search,and Multi-task learning shows that it is capable of learning robustrepresentations. This is the first effort to train a DL model on multiple tasksin parallel. Our code is available at https://github.com/MahsaPaknezhad/PaRT

PaRT: Parallel Learning Towards Robust and Transparent AI