Feature generation for long-tail classification
Rahul Vigneswaran, Marc T. Law, Vineeth Nallure Balasubramanian, Makarand Tapaswi · 2021
The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a long-tailed distribution. This imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, the limited visual diversity results in a network with poor representation ability. A simple counter to this is decoupling the representation and classifier networks and using oversampling only to train the classifier.