  We propose an end-to-end ensemble method for person re-identification (ReID)
to address the problem of overfitting in discriminative models. These models
are known to converge easily, but they are biased to the training data in
general and may produce a high model variance, which is known as overfitting.
The ReID task is more prone to this problem due to the large discrepancy
between training and test distributions. To address this problem, our proposed
ensemble learning framework produces several diverse and accurate base learners
in a single DenseNet. Since most of the costly dense blocks are shared, our
method is computationally efficient, which makes it favorable compared to the
conventional ensemble models. Experiments on several benchmark datasets
demonstrate that our method achieves state-of-the-art results. Noticeable
performance improvements, especially on relatively small datasets, indicate
that the proposed method deals with the overfitting problem effectively.
