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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Cross-Batch Reference Learning for Deep Retrieval
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Cross-Batch Reference Learning for Deep Retrieval

机译：深度检索的交叉批量参考学习

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摘要

Learning effective representations that exhibit semantic content is crucial to image retrieval applications. Recent advances in deep learning have made significant improvements in performance on a number of visual recognition tasks. Studies have also revealed that visual features extracted from a deep network learned on a large-scale image data set (e.g., ImageNet) for classification are generic and perform well on new recognition tasks in different domains. Nevertheless, when applied to image retrieval, such deep representations do not attain performance as impressive as used for classification. This is mainly because the deep features are optimized for classification rather than for the desired retrieval task. We introduce the cross-batch reference (CBR), a novel training mechanism that enables the optimization of deep networks with a retrieval criterion. With the CBR, the networks leverage both the samples in a single minibatch and the samples in the others for weight updates, enhancing the stochastic gradient descent (SGD) training by enabling interbatch information passing. This interbatch communication is implemented as a cross-batch retrieval process in which the networks are trained to maximize the mean average precision (mAP) that is a popular performance measure in retrieval. Maximizing the cross-batch mAP is equivalent to centralizing the samples relevant to each other in the feature space and separating the samples irrelevant to each other. The learned features can discriminate between relevant and irrelevant samples and thus are suitable for retrieval. To circumvent the discrete, nondifferentiable mAP maximization, we derive an approximate, differentiable lower bound that can be easily optimized in deep networks. Furthermore, the mAP loss can be used alone or with a classification loss. Experiments on several data sets demonstrate that our CBR learning provides favorable performance, validating its effectiveness.
机译：学习表现出语义含量的有效表示对于图像检索应用是至关重要的。深度学习的最新进展在许多可视识别任务上的性能取得了重大改进。研究还揭示了从深网络中提取的视觉特征在大规模的图像数据集（例如，Imagenet）上学习的用于分类是通用的，并且在不同域中的新识别任务上表现良好。然而，当应用于图像检索时，这种深度表示不会令人印象深刻地达到分类。这主要是因为深度特征是针对分类而不是所需的检索任务进行了优化。我们介绍了交叉批量参考（CBR），这是一种新的训练机制，使得能够优化具有检索标准的深网络。通过CBR，网络利用单个小纤维和其他小纤维的样品和其他用于重量更新的样本，通过实现偶然信息通过来增强随机梯度下降（SGD）训练。该偶扣通信实现为交叉批量检索过程，其中网络训练以最大化是在检索中是流行的性能测量的平均平均精度（MAP）。最大化交叉批次映射相当于集中在特征空间中彼此相关的样本，并将样品彼此不相关的样品分离。学习的特征可以区分相关和无关的样本，因此适合于检索。为了规避离散，非增强的地图最大化，我们得出了近似，可微分的下限，可以在深网络中容易地优化。此外，地图损耗可以单独使用或具有分类损耗。关于若干数据集的实验表明，我们的CBR学习提供了有利的性能，验证其有效性。

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