Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Author:

Wang Yuchao1,Wang Haochen1,Shen Yujun2,Fei Jingjing3,Li Wei3,Jin Guoqiang3,Wu Liwei3,Zhao Rui1,Le Xinyi1

Affiliation:

1. Shanghai Jiao Tong University

2. The Chinese University of Hong Kong

3. SenseTime Research

Funder

National Natural Science Foundation of China

Publisher

IEEE

Reference52 articles.

1. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results;tarvainen;Adv Neural Inform Process Syst,2017

2. Fixmatch: Simplifying semi -supervised learning with consistency and confidence;sohn;Adv Neural Inform Process Syst,2020

3. Representation learning with contrastive predictive coding;van den oord;ArXiv Preprint,2018

4. ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning

5. Evaluating bayesian deep learning methods for semantic segmentation;mukhoti;ArXiv Preprint,2018

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