A state-of-the-art review on image synthesis with generative adversarial networks
L Wang, W Chen, W Yang, F Bi, FR Yu - Ieee Access, 2020 - ieeexplore.ieee.org
Generative Adversarial Networks (GANs) have achieved impressive results in various image
synthesis tasks, and are becoming a hot topic in computer vision research because of the …
synthesis tasks, and are becoming a hot topic in computer vision research because of the …
Gan-leaks: A taxonomy of membership inference attacks against generative models
Deep learning has achieved overwhelming success, spanning from discriminative models to
generative models. In particular, deep generative models have facilitated a new level of …
generative models. In particular, deep generative models have facilitated a new level of …
Attributing fake images to gans: Learning and analyzing gan fingerprints
Abstract Recent advances in Generative Adversarial Networks (GANs) have shown
increasing success in generating photorealistic images. But they also raise challenges to …
increasing success in generating photorealistic images. But they also raise challenges to …
Generative adversarial network applications in industry 4.0: A review
The breakthrough brought by generative adversarial networks (GANs) in computer vision
(CV) applications has gained a lot of attention in different fields due to their ability to capture …
(CV) applications has gained a lot of attention in different fields due to their ability to capture …
Unleashing transformers: Parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes
Whilst diffusion probabilistic models can generate high quality image content, key limitations
remain in terms of both generating high-resolution imagery and their associated high …
remain in terms of both generating high-resolution imagery and their associated high …
Dual contrastive loss and attention for gans
Abstract Generative Adversarial Networks (GANs) produce impressive results on
unconditional image generation when powered with large-scale image datasets. Yet …
unconditional image generation when powered with large-scale image datasets. Yet …
Learning a neural 3d texture space from 2d exemplars
We suggest a generative model of 2D and 3D natural textures with diversity, visual fidelity
and at high computational efficiency. This is enabled by a family of methods that extend …
and at high computational efficiency. This is enabled by a family of methods that extend …
Beyond the spectrum: Detecting deepfakes via re-synthesis
The rapid advances in deep generative models over the past years have led to highly
{realistic media, known as deepfakes,} that are commonly indistinguishable from real to …
{realistic media, known as deepfakes,} that are commonly indistinguishable from real to …
Repmix: Representation mixing for robust attribution of synthesized images
Abstract Rapid advances in Generative Adversarial Networks (GANs) raise new challenges
for image attribution; detecting whether an image is synthetic and, if so, determining which …
for image attribution; detecting whether an image is synthetic and, if so, determining which …
A sliced wasserstein loss for neural texture synthesis
E Heitz, K Vanhoey, T Chambon… - Proceedings of the …, 2021 - openaccess.thecvf.com
We address the problem of computing a textural loss based on the statistics extracted from
the feature activations of a convolutional neural network optimized for object recognition (eg …
the feature activations of a convolutional neural network optimized for object recognition (eg …