"Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised ..."

Zhen Zhao et al. (2022)

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DOI: 10.48550/ARXIV.2212.04976

access: open

type: Informal or Other Publication

metadata version: 2024-04-02

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  - journals/corr/abs-2212-04976
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  - https://dblp.org/rec/journals/corr/abs-2212-04976
Zhen Zhao, Lihe Yang, Sifan Long, Jimin Pi, Luping Zhou, Jingdong Wang:
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation. CoRR abs/2212.04976 (2022)

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