Off-the-shelf convolutional neural network features achieve outstanding results in many image retrieval tasks. However, their invariance to target data is pre-defined by the architecture and training data. Existing approaches require fine-tuning or modification of pre-trained networks adapt variations unique In contrast, our method enhances off-the-shelf aggregating extracted from images augmen...
