Self-Paced Boost Learning for Classification

By Te Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, Yueting Zhuang. 2016

78 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Nicu Sebe
2. Radu Tudor Ionescu
3. Eric Learned-Miller
4. Wenwu Zhu
5. Xin Wang
6. Haw-Shiuan Chang
7. Yudong Chen
8. Petru Soviany

Citations in the corpus

• Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples (2017, methodology): “By decreasing the weight of difficult examples in the loss function, the model may become more robust to outliers [33], and this method has proven useful in several applications, especially with noisy labels [36].”
• A Survey on Curriculum Learning (2021, methodology): “Moreover, as shown in [65], [85], [110], CL methods can be incorporated with other strategies”
• Under review as a conference paper at ICLR 2020 many domain adaptation methods (2019, methodology): “The ensemble of multiple learners has widely been used to avoid the overfitting problem Singh et al. (2016); Han et al. (2017; 2016); Pi et al. (2016).”
• Curriculum Learning: A Survey (2021, methodology): “[33] combine boosting with self-paced learning in order to build the self-paced boost learning methodology for classification.”