9 datasets found

Text-to-image models, such as DDIM or PLMS.

Language-guided image diffusion models, such as OpenCLIP and LAION-5B.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a generative model dataset.

LatentGAN Autoencoder: Learning Disentangled Latent Distribution

The dataset used in this paper is the CIFAR10 dataset, which contains 60,000 32x32 color images in 10 classes, with 6,000 images per class.

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

The CLIP model and its variants are becoming the de facto backbone in many applications. However, training a CLIP model from hundreds of millions of image-text pairs can be...

The dataset used in the paper for training and testing the proposed method for detecting, attributing and localizing GAN generated images.

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...