Feb. 7, 2024, 5:47 a.m. | Dayou Mao Yuhao Chen Yifan Wu Maximilian Gilles Alexander Wong

cs.CV updates on arXiv.org arxiv.org

Multi-task learning (MTL) has been widely studied in the past decade. In particular, dozens of optimization algorithms have been proposed for different settings. While each of them claimed improvement when applied to certain models on certain datasets, there is still lack of deep understanding on the performance in complex real-worlds scenarios. We identify the gaps between research and application and make the following 4 contributions. (1) We comprehensively evaluate a large set of existing MTL optimization algorithms on the MetaGraspNet …

algorithms analysis cs.cv datasets improvement multi-task learning optimization performance robust them understanding vision

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