PaRT：面向健壮透明的并行学习AI
PaRT: Parallel Learning Towards Robust and Transparent AI
摘要：
本文采用了一种并行学习方法来实现健壮和透明的AI。深度神经网络在多个任务上并行训练，其中每个任务仅在网络资源的子集上训练。每个子集由网段组成，可以在特定任务中组合和共享。任务可以与其他任务共享资源，同时拥有独立的任务相关网络资源。因此，经过训练的网络可以在各种任务中共享类似的表示，同时还可以实现独立的任务相关表示。上述情况促成了一些关键的结果。(1)我们的方法的平行性质否定了灾难性遗忘的问题。(2)段的共享更有效地使用网络资源。(3)我们表明，网络确实通过共享表示在其他任务中使用从某些任务中学到的知识。(4)通过检查与单个任务相关的和共享的表示，该模型提供了网络以及多任务环境中任务之间关系的透明度。针对复杂的竞争方法，如持续学习，神经结构搜索和多任务学习的评估表明，它是能够学习强大的表示。这是第一次对并行多任务的DL模型进行训练。我们的代码可在https://github.com/MahsaPaknezhad/PaRT上获得
英文摘要：
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 task is trained only on a subset of the network resources. Each subset consists of network segments, that can be combined and shared across specific tasks. Tasks can share resources with other tasks, while having independent task-related network resources. Therefore, the trained network can share similar representations across various tasks, while also enabling independent task-related representations. The above allows for some crucial outcomes. (1) The parallel nature of our approach negates the issue of catastrophic forgetting. (2) The sharing of segments uses network resources more efficiently. (3) We show that the network does indeed use learned knowledge from some tasks in other tasks, through shared representations. (4) Through examination of individual task-related and shared representations, the model offers transparency in the network and in the relationships across tasks in a multi-task setting. Evaluation of the proposed approach against complex competing approaches such as Continual Learning, Neural Architecture Search, and Multi-task learning shows that it is capable of learning robust representations. This is the first effort to train a DL model on multiple tasks in parallel. Our code is available at https://github.com/MahsaPaknezhad/PaRT
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
