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Found 7 papers, 7 papers with code
Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks
Although Negative Data Augmentation (NDA) effectively enhances unconditional and class-conditional GANs by introducing anomalies into real training images, guiding the GANs away from low-quality outputs, its impact on CcGANs is limited, as it fails to replicate negative samples that may occur during the CcGAN sampling.
MixFlows: principled variational inference via mixed flows
This work presents mixed variational flows (MixFlows), a new variational family that consists of a mixture of repeated applications of a map to an initial reference distribution.
Bayesian inference via sparse Hamiltonian flows
A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during Bayesian inference, with the goal of reducing computational cost.
The computational asymptotics of Gaussian variational inference and the Laplace approximation
Gaussian variational inference and the Laplace approximation are popular alternatives to Markov chain Monte Carlo that formulate Bayesian posterior inference as an optimization problem, enabling the use of simple and scalable stochastic optimization algorithms.
Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression
Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher.
Bayesian Pseudocoresets
Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern large-scale data.
Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms
This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels).
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Image Generation
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