In this paper, we propose a novel deep generative approach to cross-modal
retrieval to learn hash functions in the absence of paired training samples
through the cycle consistency loss. Our proposed approach employs adversarial
training scheme to lean a couple of hash functions enabling translation between
modalities while assuming the underlying semantic relationship. To induce the
hash codes with semantics to the input-output pair, cycle consistency loss is
further proposed upon the adversarial training to strengthen the correlations
between inputs and corresponding outputs. Our approach is generative to learn
hash functions such that the learned hash codes can maximally correlate each
input-output correspondence, meanwhile can also regenerate the inputs so as to
minimize the information loss. The learning to hash embedding is thus performed
to jointly optimize the parameters of the hash functions across modalities as
well as the associated generative models. Extensive experiments on a variety of
large-scale cross-modal data sets demonstrate that our proposed method achieves
better retrieval results than the state-of-the-arts.

本文提出了一种新的深度生成式方法，通过循环一致性损失来学习哈希函数，实现无成对训练样本的跨模态检索，该方法利用对抗训练来学习一组哈希函数，从而实现不同模态之间的转换，并进一步提出循环一致性损失来构造具有语义的哈希码，从而在大规模跨模态数据集上实现更好的检索结果。

循环一致性深度生成哈希用于跨模态检索

Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

Recent progresses on deep discriminative and generative modeling have shown
promising results on texture synthesis. However, existing feed-forward based
methods trade off generality for efficiency, which suffer from many issues,
such as shortage of generality (i.e., build one network per texture), lack of
diversity (i.e., always produce visually identical output) and suboptimality
(i.e., generate less satisfying visual effects). In this work, we focus on
solving these issues for improved texture synthesis. We propose a deep
generative feed-forward network which enables efficient synthesis of multiple
textures within one single network and meaningful interpolation between them.
Meanwhile, a suite of important techniques are introduced to achieve better
convergence and diversity. With extensive experiments, we demonstrate the
effectiveness of the proposed model and techniques for synthesizing a large
number of textures and show its applications with the stylization.

本文提出了一种基于深度生成式前馈网络实现的纹理综合方法，具有在一个网络内高效综合不同纹理并进行有意义插值的能力，并通过多项实验证明了提出的模型和技术的有效性和应用前缀。