The following three steps are repeated until convergence: 1) the database network encodes all training samples into binary codes to obtain whole rank list, 2) the query network is trained based on policy learning to maximize a reward that indicates the performance of the whole ranking list of binary codes, e.g., mean average precision (MAP), and 3) the database network is updated as the query network. In this paper, we present a novel deep policy hashing architecture with two systems are learned in parallel: aquery network and a shared and slowly changingdatabase network.
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However, learning deep hashing with listwise supervision is challenging in 1) how to obtain the rank list of whole training set when the batch size of the deep network is always small and 2) how to utilize the listwise supervision. These manners ignore the fact that hashing is a prediction task on the list of binary codes. The pairwise and triplet losses are two widely used similarity preserving manners for deep hashing.
![hash code pda net hash code pda net](https://media.geeksforgeeks.org/wp-content/cdn-uploads/PDA_1.png)
The experimental results on two large datasets (up to one million samples) demonstrate its superior performance over state-of-the-art supervised and unsupervised methods.ĭeep-networks-based hashing has become a leading approach for large-scale image retrieval, which learns a similarity-preserving network to map similar images to nearby hash codes. The proposed method can handle both metric as well as semantic similarity. In this work, we propose a semi-supervised hashing method that is formulated as minimizing empirical error on the labeled data while maximizing variance and independence of hash bits over the labeled and unlabeled data. Moreover, these methods are usually very slow to train.
![hash code pda net hash code pda net](https://www.cancerimagingarchive.net/wp-content/uploads/2019/12/TCIA-logo-2x.png)
There exist supervised hashing methods that can handle such semantic similarity but they are prone to overfitting when labeled data is small or noisy. Unsupervised hashing methods show good performance with metric distances but, in image search, semantic similarity is usually given in terms of labeled pairs of images. Several hashing methods have been proposed to allow approximate but highly efficient search. Large scale image search has recently attracted considerable attention due to easy availability of huge amounts of data. The results demonstrate significant improvements in the performance of our method relative to other fine-grained hashing algorithms. Extensive experiments are conducted on two public benchmark fine-grained datasets. Moreover, to better capture subtle differences, multi-scale regions at different layers are learned without the need of bounding-box/part annotations. The hash coding module aims to generate effective binary codes and give feedback for learning better localizer. The region localization module aims to provide informative regions to the hash coding module.
![hash code pda net hash code pda net](https://i.ytimg.com/vi/ioDroq1VZIA/hqdefault.jpg)
The proposed approach consists of a region localization module and a hash coding module. In this paper, we propose a deep fine-grained hashing to simultaneously localize the discriminative regions and generate the efficient binary codes. While these two tasks are correlated and can reinforce each other. Most existing deep hashing approaches solve the two tasks independently. Fine-grained image hashing is a challenging problem due to the difficulties of discriminative region localization and hash code generation.