End-to-End Training of CNN Ensembles for Person Re-Identification

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.

CNN集成人员的端到端培训，以重新识别人

我们提出了一种用于人员重新识别（ReID）的端到端集成方法，以解决判别模型中的过拟合问题。已知这些模型易于收敛，但是它们通常会偏向训练数据，并且可能会产生较大的模型方差，这称为过拟合。.. 由于训练和测试分布之间的巨大差异，ReID任务更容易出现此问题。为了解决这个问题，我们提出的集成学习框架可以在一个DenseNet中生成多个不同且准确的基础学习器。由于大多数昂贵的密集块是共享的，因此我们的方法具有较高的计算效率，与传统的集成模型相比，它是有利的。在几个基准数据集上进行的实验表明，我们的方法可以达到最新的结果。性能的显着提高，尤其是在相对较小的数据集上，表明该方法有效地解决了过拟合问题。 （阅读更多）