10 datasets found

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the CelebA-HQ, ImageNet, and Place2 datasets.

The Inpaint Anything dataset is a large-scale image inpainting dataset, containing images with missing regions.

Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like...

The CelebAHQ dataset was used with a fixed removal mask located near the image centers [11].

Image inpainting for irregular holes using partial convolutions.

The Berkeley Image Segmentation Dataset (BSDS) is a standard benchmarking data set for many computer vision image segmentation algorithms.

Image-to-image translation is an important and challenging problem in computer vision and image processing. Diffusion models have shown great potentials for high-quality image...

Single Image Super-Resolution (SR) aims to generate a High Resolution (HR) image I SR from a low resolution (LR) im-age I LR such that it is similar to original HR image I HR....

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

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...