Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Generative adversarial nets"”.
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Three ploys for robust co-generation with generative adversarial nets
Generative adversarial nets (GANs) and variational auto-encoders enable accurate modeling of high-dimensional data distributions by forward propagating a sample drawn from a latent space. However, an often overlooked shortcoming is their inability to find an arbitrary marginal distribution, which …
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Co-generation with GANs using AIS based HMC
… high-dimensional distributions, particularly generative adversarial nets (GANs). Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation …
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Generative modeling using the sliced Wasserstein distance
Generative adversarial nets (GANs) are very successful at modeling distributions from given samples, even in the high-dimensional case. However, their formulation is also known to be hard to optimize and often unstable. While the aforementioned problems are particularly true for early GAN …
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Data generalization for new classes with a single instance via automatic style labeling and transfer
… new images from a specific class, most generative models like Generative Adversarial Nets (GANs) require a large amount of data from this class. In other words, modern generative models often lack the ability to create new samples belonging to an unseen class from which they have …
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Dynamic image crowd representations for improved anomaly detection using generative adversarial networks
… translation using CGANs (Conditional generative adversarial nets) for anomaly detection within crowds, and the proposed framework is evaluated on benchmark datasets as well as the AHDCrowd dataset. The applied experiments evaluate the effectiveness of utilising various types of dynamic …
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Deep Generative Models and Biological Applications
… in a wide variety of applications. </p><p>Generative models provide an excellent manipulation method for training from rich available unlabeled data set and sampling new data points from underlying high-dimensional probability distributions. </p><p>The recent proposed Variational …