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Very deep neural networks with hundreds or more layers have achieved significant success in a variety of vision tasks spanning from image classification, detection, to image captioning. However, simply stacking more layers in the convolution operation could suffer from the gradient vanishing problem and thus could not lower down the training loss further. The residual network [1] pushes the model's...
Image retrieval and classification are hot topics in computer vision and have attracted great attention nowadays with the emergence of large-scale data. We propose a new scheme to use both deep learning models and large-scale computing platform and jointly learn powerful feature representations in image classification and retrieval. We achieve a superior performance on the ImageNet dataset, where...
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