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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Sample Balancing for Deep Learning-Based Visual Recognition
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Sample Balancing for Deep Learning-Based Visual Recognition

机译：基于深度学习的视觉识别的样本平衡

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摘要

Sample balancing includes sample selection and sample reweighting. Sample selection aims to remove some bad samples that may lead to bad local optima. Sample reweighting aims to assign optimal weights to samples to improve performance. In this article, we integrate a sample selection method based on self-paced learning into deep learning frameworks and study the influence of different sample selection strategies on training deep networks. In addition, most of the existing sample reweighting methods mainly take per-class sample number as a metric, which does not fully consider sample qualities. To improve the performance, we propose a novel metric based on the multiview semantic encoders to reweight the samples more appropriately. Then, we propose an optimization mechanism to embed sample weights into loss functions of deep networks, which can be trained in end-to-end manners. We conduct experiments on the CIFAR data set and the ImageNet data set. The experimental results demonstrate that our proposed sample balancing method can improve the performances of deep learning methods in several visual recognition tasks.
机译：样本平衡包括样本选择和样本重新重量。样本选择旨在删除可能导致本地最佳OptimA不佳的一些不良样本。样本重新传递旨在为样本分配最佳权重，以提高性能。在本文中，我们将基于自花奏学习的样本选择方法集成到深度学习框架中，并研究不同样本选择策略对训练深网络的影响。此外，大多数现有的样本重新重量方法主要采用每级样本号作为指标，这不完全考虑样本质量。为了提高性能，我们提出了一种基于多视图语义编码器的新型度量，以更适当地重新调度样品。然后，我们提出了一种优化机制，以将样本权重嵌入深网络的损耗功能，这可以以端到端的方式训练。我们对CiFar数据集进行实验和ImageNet数据集。实验结果表明，我们所提出的样本平衡方法可以提高几种可视识别任务中深度学习方法的性能。

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