Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learning
Table 1: Scores for imbalanced data produced from ImageNet
['[EMPTY]', 'Accuracy', 'Precision', 'Recall', 'F1']
We used the Resnet34 neural network, an optimizer of Adam, no data augmentation, a batch size of 256, and an epoch of 100. The baseline values in the table are for the as- is imbalanced data and accuracy for the original test data. The precision, recall, F1 are macro averages over each class; therefore, they are low. However, they are improved by cavity filling. Cavity filling improves the accuracy by 4.3% from baseline, making it better than SMOTE.