The GAN Hinge Loss is a hinge loss based loss function for generative adversarial networks: $$ L_{D} = -\mathbb{E}_{\left(x, y\right)\sim{p}_{data}}\left[\min\left(0, -1 + D\left(x, y\right)\right)\right] -\mathbb{E}_{z\sim{p_{z}}, y\sim{p_{data}}}\left
Building on these findings, we introduce HingeRLC-GAN, a novel approach that combines RLC Regularization and the Hinge loss function. With a FID Score of 18 and a KID Score of 0.001, our approach outperforms existing methods by effectively balancing training stability and increased diversity.Goni...
TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs).This code implements cGANs with Multi-Hinge Loss from this paper, for fully and semi supervised settings. It uses the Imagenet, Cifar100, Cifar10 datasets....
This is my TensorFlow implementations of Wasserstein GANs with Gradient Penalty (WGAN-GP) proposed in Improved Training of Wasserstein GANs, Least Squares GANs (LSGAN), and GANs with the hinge loss.The key insight of WGAN-GP is as follows. To enforce Lipschitz constraint in Wasserstein GAN, ...
To balance above both problems, a network with model transfer using the GAN-WP and non-greedy loss is introduced in this paper. Firstly, inspired by the Support Vector Machine's mechanism, multi-hinge loss is used during training stage. Then, instead of directly training a deep neural ...
And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets, and evaluate the results using SSIM and PSNR. The experimental results show that the ...