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NYUv2

Multi-task learning (MTL) research is broadly divided into two categories: one is to learn the correlation between tasks through model structures, and the other is to balance the joint training process of all tasks through optimization algorithms.

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Cite this as

Jiaming Liu, Qizhe Zhang, Jianing Li, Ming Lu, Tiejun Huang, Shanghang Zhang (2024). Dataset: NYUv2. https://doi.org/10.57702/7osc5sda

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Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2007.05441
Citation
  • https://doi.org/10.48550/arXiv.1901.10034
  • https://doi.org/10.48550/arXiv.2307.15429
  • https://doi.org/10.48550/arXiv.2007.13727
  • https://doi.org/10.48550/arXiv.2306.05242
  • https://doi.org/10.48550/arXiv.2009.09976
  • https://doi.org/10.48550/arXiv.2211.14037
  • https://doi.org/10.48550/arXiv.2002.12114
Author Jiaming Liu
More Authors
Qizhe Zhang
Jianing Li
Ming Lu
Tiejun Huang
Shanghang Zhang
Homepage https://www.cs.nyu.edu/~ylclab/research/datasets/nyu-depth-v2.html