Semi-supervised Cross-modal Hashing Method for Semantic Alignment Networks Basedon GAN

Supervised methods have achieved a lot of results in cross-modal retrieval and have become popular methods.How-ever,these methods rely too much on labeled data and do not make full use of the rich information contained in unlabeled data.To solve this problem,unsupervised methods have been studied,but when relying solely on unlabeled data,the results are not ideal.Therefore,this paper proposes a semi-supervised cross-modal hashing method for semantic alignment networks based on GAN(GAN-SASCH).This model is based on generative adversarial networks that incorporate the concept of semantic alignment.The generative adversarial network is divided into two modules.The generator learns to fit the correlation distribution of the unlabeled data and generates a spurious data sample,and the discriminator is used to determine whether the data pair sample comes from the dataset or the generator.By developing a very small adversarial game between these two modules,the performance of the ge-nerative adversarial network is continuously improved.Semantic alignment can make full use of the interaction and symmetry between different modalities,unify the similarity information of different modalities,and effectively guide the learning process of hash code.In this paper,adaptive learning optimization parameters are also introduced to improve the performance of the model.On NUS-WIDE and MIRFLICKR25K datasets,we compare the proposed method with 9 related frontier methods,and verify the effectiveness of the proposed method by using two evaluation indicators,MAP and PR map.