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Learning Versatile Neural Architectures by Propagating Network Codes
[article]
2022
arXiv
pre-print
This work explores how to design a single neural network capable of adapting to multiple heterogeneous vision tasks, such as image segmentation, 3D detection, and video recognition. This goal is challenging because both network architecture search (NAS) spaces and methods in different tasks are inconsistent. We solve this challenge from both sides. We first introduce a unified design space for multiple tasks and build a multitask NAS benchmark (NAS-Bench-MR) on many widely used datasets,
arXiv:2103.13253v2
fatcat:6mxtixe4yvfjthjuzyv23c2psa