Feature Transfer Learning for Deep Face Recognition with Under-Represented Data

By Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker. 2018

45 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. Zheda Mai
2. Fan Yang
3. Chris Dongjoo Kim
4. Kihyuk Sohn
5. Gunhee Kim
6. Alan L. Yuille
7. Ruiwen Li
8. Da-Wei Zhou

Citations in the corpus

• Online Continual Learning in Image Classification: An Empirical Survey (2021, background): “Other than the bias related to the Softmax classifier mentioned above, another problem induced by class imbalance is under-representation of the minority [95, 96], where minority classes do not show a discernible pattern in the latent feature space [63].”
• Imbalanced Continual Learning with Partitioning Reservoir Sampling (2020, background): “Mainly due to the lack of data, the minority classes are much under-represented within the learned features relative to the majority [69,19].”
• CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning (2021, background): “Some works [50, 44, 23, 28, 29] aim to transfer knowledge from majority classes to minority classes.”