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Found 6 papers, 3 papers with code
Scaling Up LLM Reviews for Google Ads Content Moderation
This study proposes a method for scaling up LLM reviews for content moderation in Google Ads.
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data
In contrast, we propose a Discriminator gradIent Gap regularized GAN (DigGAN) formulation which can be added to any existing GAN.
Precondition Layer and Its Use for GANs
We use this PC-layer in two ways: 1) fixed preconditioning (FPC) adds a fixed PC-layer to all layers, and 2) adaptive preconditioning (APC) adaptively controls the strength of preconditioning.
Towards a Better Global Loss Landscape of GANs
We also perform experiments to support our theory that RpGAN has a better landscape than separable-GAN.
Co-Generation with GANs using AIS based HMC
Inferring the most likely configuration for a subset of variables of a joint distribution given the remaining ones - which we refer to as co-generation - is an important challenge that is computationally demanding for all but the simplest settings.
CP-GAN: Towards a Better Global Landscape of GANs
In this work, we perform a global analysis of GANs from two perspectives: the global landscape of the outer-optimization problem and the global behavior of the gradient descent dynamics.
