  Training datasets are crucial for convolutional neural network-based
algorithms, which directly impact their overall performance. As such, using a
well-structured dataset that has minimum level of bias is always desirable. In
this paper, we proposed a novel, domain-independent approach, called
score-based resampling (SBR), to locate the under-represented samples of the
original training dataset based on the model prediction scores obtained with
that training set. In our method, once trained, we use the same CNN model to
infer on its own training samples, obtain prediction scores, and based on the
distance between predicted and ground-truth, we identify samples that are far
away from their ground-truth and augment them in the original training set. The
temperature term of the Sigmoid function is decreased to better differentiate
scores. For experimental evaluation, we selected one Kaggle dataset for gender
classification. We first used a CNN-based classifier with relatively standard
structure, trained on the training images, and evaluated on the provided
validation samples of the original dataset. Then, we assessed it on a totally
new test dataset consisting of light male, light female, dark male, and dark
female groups. The obtained accuracies varied, revealing the existence of
categorical bias against certain groups in the original dataset. Subsequently,
we trained the model after resampling based on our proposed approach. We
compared our method with a previously proposed variational autoencoder (VAE)
based algorithm. The obtained results confirmed the validity of our proposed
method regrading identifying under-represented samples among original dataset
to decrease categorical bias of classifying certain groups. Although tested for
gender classification, the proposed algorithm can be used for investigating
dataset structure of any CNN-based tasks.
