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Found 5 papers, 5 papers with code
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
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Video Generation
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Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution.
Covariance Pooling For Facial Expression Recognition
In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition.
Facial Expression Recognition
Facial Expression Recognition (FER)
Wasserstein Divergence for GANs
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance.
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
