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User portrait has become a research hot spot in the field of knowledge graph in recent years and the rationality of tag extraction directly affects the quality of user portrait. However, most of the current tag extraction methods for portraits only consider the methods based on word frequency statistics and semantic clustering. These methods have some drawbacks: they cannot effectively discover the preferred themes of the enterprise, dynamically update the portrait tags, and adapt to the needs of the enterprise. In this paper, we propose an enterprise adaptive tag extraction method based on multi-feature dynamic portrait (ATEMDP). ATEMDP first uses K-means to measure the similarity between enterprise texts in preference division, and converts similar enterprise text clustering problems into tag feature clusters to obtain the point cluster structure containing the distribution of tag preference topics. In addition, in the multi-feature selection, the professional domain thesaurus is introduced for feature expansion, and the topic text is introduced into the Bert model as a sample set to discover the potential features of the enterprise text. In the end, in dynamic tag extraction, BiLSTM and CNN are used to extract features, and dynamic preference tags are obtained by updating enterprise text. THUCNews data set and Ente-pku data set are used for simulation, and seven other methods are considered in comparison. The experimental results indicate that ATEMDP is not only superior to other conventional methods in accuracy and F1-score, but also effectively solves the dynamic tagging problem of enterprise portrait. [ABSTRACT FROM AUTHOR]

Abstract User portrait has become a research hot spot in the field of knowledge graph in recent years and the rationality of tag extraction directly affects the quality of user portrait. However, most of the current tag extraction methods for portraits only consider the methods based on word frequency statistics and semantic clustering. These methods have some drawbacks: they cannot effectively discover the preferred themes of the enterprise, dynamically update the portrait tags, and adapt to the needs of the enterprise. In this paper, we propose an enterprise adaptive tag extraction method based on multi-feature dynamic portrait (ATEMDP). ATEMDP first uses K-means to measure the similarity between enterprise texts in preference division, and converts similar enterprise text clustering problems into tag feature clusters to obtain the point cluster structure containing the distribution of tag preference topics. In addition, in the multi-feature selection, the professional domain thesaurus is introduced for feature expansion, and the topic text is introduced into the Bert model as a sample set to discover the potential features of the enterprise text. In the end, in dynamic tag extraction, BiLSTM and CNN are used to extract features, and dynamic preference tags are obtained by updating enterprise text. THUCNews data set and Ente-pku data set are used for simulation, and seven other methods are considered in comparison. The experimental results indicate that ATEMDP is not only superior to other conventional methods in accuracy and F1-score, but also effectively solves the dynamic tagging problem of enterprise portrait.

Unpaired image-to-image translation for the generation field has made much progress recently. However, these methods suffer from mode collapse because of the overfitting of the discriminator. To this end, we propose a straightforward method to construct a contrastive loss using the feature information of the discriminator output layer, which is named multi-feature contrastive learning (MCL). Our proposed method enhances the performance of the discriminator and solves the problem of model collapse by further leveraging contrastive learning. We perform extensive experiments on several open challenge datasets. Our method achieves state-of-the-art results compared with current methods. Finally, a series of ablation studies proved that our approach has better stability. In addition, our proposed method is also practical for single image translation tasks. Code is available at https://github.com/gouayao/MCL. [ABSTRACT FROM AUTHOR]

Abstract Unpaired image-to-image translation for the generation field has made much progress recently. However, these methods suffer from mode collapse because of the overfitting of the discriminator. To this end, we propose a straightforward method to construct a contrastive loss using the feature information of the discriminator output layer, which is named multi-feature contrastive learning (MCL). Our proposed method enhances the performance of the discriminator and solves the problem of model collapse by further leveraging contrastive learning. We perform extensive experiments on several open challenge datasets. Our method achieves state-of-the-art results compared with current methods. Finally, a series of ablation studies proved that our approach has better stability. In addition, our proposed method is also practical for single image translation tasks. Code is available at https://github.com/gouayao/MCL.