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Found 5 papers, 0 papers with code
Text-to-image Synthesis via Symmetrical Distillation Networks
For addressing these problems, we exploit the excellent capability of generic discriminative models (e. g. VGG19), which can guide the training process of a new generative model on multiple levels to bridge the two gaps.
SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network
(2) Ignore the rich information contained in the large amount of unlabeled data across different modalities, especially the margin examples that are easily to be incorrectly retrieved, which can help to model the correlations.
Unsupervised Generative Adversarial Cross-modal Hashing
To address the above problem, in this paper we propose an Unsupervised Generative Adversarial Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for unsupervised representation learning to exploit the underlying manifold structure of cross-modal data.
MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval
Transfer learning is for relieving the problem of insufficient training data, but it mainly focuses on knowledge transfer only from large-scale datasets as single-modal source domain to single-modal target domain.
Cross-modal Common Representation Learning by Hybrid Transfer Network
Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process.
