  In this paper, we aim to improve the performance of a deep learning model
towards image classification tasks, proposing a novel anchor-based training
methodology, named \textit{Online Anchor-based Training} (OAT). The OAT method,
guided by the insights provided in the anchor-based object detection
methodologies, instead of learning directly the class labels, proposes to train
a model to learn percentage changes of the class labels with respect to defined
anchors. We define as anchors the batch centers at the output of the model.
Then, during the test phase, the predictions are converted back to the original
class label space, and the performance is evaluated. The effectiveness of the
OAT method is validated on four datasets.
