在上述工作的基础上，OpenAI于2022年发表了论文《GLIDE: Towards Photorealistic Image Generation and Editi...
To induce the generator to discover the distinctions between classes, we construct semantically congruent and incongruent pairs in the generation process, and further regularize the generator by encouraging high similarities of congruent pairs, while penalizing that of incongruent ones in the classifier's...
Through numerical simulations on Gaussian mixtures and experiments on class-conditional and text-to-image diffusion models, we validate our analysis and show that our non-linear CFG offers improved flexibility and generation quality without additional computation cost...#扩散模型 #文生图 发布于...
4, Use the seconde Discriminator D1 Another case: transform from 1 image to another, with a certain goal. For traditional supervised approach, the output image is blurry, because it's the average of several images. Image generated by generator has to be not only clear enough to pass discrim...
Our model is designed with unique neural network structures, image features and training method. To validate the performance of our model, we utilized it in Chinese handwriting generation, and an evaluation method called mean opinion score (MOS) was used. The MOS results show that MSMC-CGAN ...
ContraGAN: Contrastive Learning for Conditional Image Generation. In Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 6–12 December 2020; pp. 1–13. [Google Scholar] Ding, X.; Wang, Y.; Xu, Z.; Welch, W.J.; Wang, Z.J. CcGAN: Continuous ...
ContraGAN: Contrastive Learning for Conditional Image Generation. In Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 6–12 December 2020; pp. 1–13. [Google Scholar] Ding, X.; Wang, Y.; Xu, Z.; Welch, W.J.; Wang, Z.J. CcGAN: Continuous ...
This kind of GAN is used for generating a specific type of image. In this work we have used cGAN for generating Class Based Character Generation. This work can help researchers to generate handwritten characters to enhance the perfomance of deep learning models. We have trained this model to ...
In this paper, we propose a new model for conditional video generation (GammaGAN). Generally, it is challenging to generate a plausible video from a single image with a class label as a condition. Traditional methods based on conditional generative adversarial networks (cGANs) often ...
[Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching]: Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation but suffer from slow generation due to the token-by-token process. We ask an ambitious question: can...