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Found 5 papers, 4 papers with code
EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders
We evidentially monitor the significance of attaining the probability distribution over the codebook embeddings, in contrast to softmax usage.
ProtoDiffusion: Classifier-Free Diffusion Guidance with Prototype Learning
Diffusion models are generative models that have shown significant advantages compared to other generative models in terms of higher generation quality and more stable training.
Textile Pattern Generation Using Diffusion Models
The fine-tuned diffusion model is trained with this newly created dataset, and its results are compared with the baseline models visually and numerically.
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks
Lastly, we show the contribution of the self-supervision tasks to the GAN training on the loss landscape and present that the effects of these tasks may not be cooperative to the adversarial training in some settings.
DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning
Generative Adversarial Networks (GANs) triggered an increased interest in problem of image generation due to their improved output image quality and versatility for expansion towards new methods.
