The pretrained models from four image translation algorithms: ACL-GAN, Council-GAN, CycleGAN, and U-GAT-IT on three benchmarking datasets: Selfie2Anime, CelebA_gender, CelebA_glasses. We trained the models to provide benchmarks for the algorithm we detailed in the paper "UVCGAN: UNet Vision Transformer Cycle-consistent GAN for Unpaired Image-to-Image Translation.". We only trained a model if a pretrained model is provided by a benchmarking algorithm
Recently, Conditional generative adversarial network (cGAN) plays an important role in image synthes...
We present an application of conditional generative adversarial network (cGAN) to produce photo-real...
CVPR 2021 oralInternational audienceCoMoGAN is a continuous GAN relying on the unsupervised reorgani...
Pre-trained models of the "Rethinking CycleGAN: Improving Quality of GANs for Unpaired Image-to-Imag...
Unpaired image-to-image translation has broad applications in art, design, and scientific simulation...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
The advent of large-scale training has produced a cornucopia of powerful visual recognition models. ...
We propose to use pretraining to boost general image-to-image translation. Prior image-to-image tran...
The files are pretrained network weights of UVCGAN on the Simple Liquid Argon Track Samples (SLATS) ...
Generative Adversarial Networks (GANs) have recently introduced effective methods of performing Imag...
Generative Adversarial Network is the topic of interest in today’s research in the field of image pr...
In this thesis, we study approaches to learn priors on data (i.e. generative modeling) and learners ...
Transfer learning has become an important technique in computer vision, allowing models to take know...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Given the dependency of current CNN architectures on a large training set, the possibility of usin...
Recently, Conditional generative adversarial network (cGAN) plays an important role in image synthes...
We present an application of conditional generative adversarial network (cGAN) to produce photo-real...
CVPR 2021 oralInternational audienceCoMoGAN is a continuous GAN relying on the unsupervised reorgani...
Pre-trained models of the "Rethinking CycleGAN: Improving Quality of GANs for Unpaired Image-to-Imag...
Unpaired image-to-image translation has broad applications in art, design, and scientific simulation...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
The advent of large-scale training has produced a cornucopia of powerful visual recognition models. ...
We propose to use pretraining to boost general image-to-image translation. Prior image-to-image tran...
The files are pretrained network weights of UVCGAN on the Simple Liquid Argon Track Samples (SLATS) ...
Generative Adversarial Networks (GANs) have recently introduced effective methods of performing Imag...
Generative Adversarial Network is the topic of interest in today’s research in the field of image pr...
In this thesis, we study approaches to learn priors on data (i.e. generative modeling) and learners ...
Transfer learning has become an important technique in computer vision, allowing models to take know...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Given the dependency of current CNN architectures on a large training set, the possibility of usin...
Recently, Conditional generative adversarial network (cGAN) plays an important role in image synthes...
We present an application of conditional generative adversarial network (cGAN) to produce photo-real...
CVPR 2021 oralInternational audienceCoMoGAN is a continuous GAN relying on the unsupervised reorgani...
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