论文标题
CNN合奏的端到端培训人员重新识别
End-to-End Training of CNN Ensembles for Person Re-Identification
论文作者
论文摘要
我们提出了一种人重新识别（REID）的端到端集合方法，以解决在判别模型中过度拟合的问题。已知这些模型很容易收敛，但它们通常会偏向训练数据，并可能产生高模型差异，这被称为过度拟合。由于培训和测试分布之间的差异，REID任务更容易解决此问题。为了解决这个问题，我们提出的合奏学习框架在单个Densenet中产生了几种多样而准确的基础学习者。由于共享大多数昂贵的密集块，因此我们的方法在计算上是有效的，这使其与常规集合模型相比有利。几个基准数据集的实验表明，我们的方法可实现最新的结果。明显的性能改进，尤其是在相对较小的数据集上，表明所提出的方法有效地处理了过度拟合的问题。
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.
