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In contrast to previous works employing\npre-trained deep networks as a black box to produce features, our method\nleverages a deep architecture trained for the specific task of image retrieval.\nOur contribution is twofold: (i) we leverage a ranking framework to learn\nconvolution and projection weights that are used to build the region features;\nand (ii) we employ a region proposal network to learn which regions should be\npooled to form the final global descriptor. We show that using clean training\ndata is key to the success of our approach. To that aim, we use a large scale\nbut noisy landmark dataset and develop an automatic cleaning approach. The\nproposed architecture produces a global image representation in a single\nforward pass. Our approach significantly outperforms previous approaches based\non global descriptors on standard datasets. It even surpasses most prior works\nbased on costly local descriptor indexing and spatial verification. 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