Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning

By Han S. Lee, Minseok Park, Junmo Kim. 2016

197 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

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

In Artificial Intelligence

1. Xiao Wang
2. Lixu Wang
3. Zhi Xu
4. Qi Zhu
5. Yuzhe Yang
6. Shichao Xu
7. Jaya Kalla
8. Soma Biswas

In Signal Processing

1. Zhi Xu
2. Yuzhe Yang

Citations in the corpus

• Addressing Class Imbalance in Federated Learning (2020, methodology): “Data-level approaches leverage data re-sampling (Van Hulse, Khoshgoftaar, and Napolitano 2007; Mani and Zhang 2003) and data augmentation (Lee, Park, and Kim 2016; Pouyanfar et al. 2018).”
• Rethinking the Value of Labels for Improving Class-Imbalanced Learning (2020, methodology): “Classical methods have been focusing on designing data re-sampling strategies, including over-sampling the minority classes [2,41,44] as well as under-sampling the frequent classes [5,31].”