Instance-conditioned gan
Abstract Generative Adversarial Networks (GANs) can generate near photo realistic images
in narrow domains such as human faces. Yet, modeling complex distributions of datasets …
in narrow domains such as human faces. Yet, modeling complex distributions of datasets …
Semi-supervised vision transformers at scale
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored
topic despite the wide adoption of the ViT architectures to different tasks. To tackle this …
topic despite the wide adoption of the ViT architectures to different tasks. To tackle this …
Improving generalization and stability of generative adversarial networks
Generative Adversarial Networks (GANs) are one of the most popular tools for learning
complex high dimensional distributions. However, generalization properties of GANs have …
complex high dimensional distributions. However, generalization properties of GANs have …
Creating artificial human genomes using generative neural networks
Generative models have shown breakthroughs in a wide spectrum of domains due to recent
advancements in machine learning algorithms and increased computational power. Despite …
advancements in machine learning algorithms and increased computational power. Despite …
Interpolation-based contrastive learning for few-label semi-supervised learning
Semi-supervised learning (SSL) has long been proved to be an effective technique to
construct powerful models with limited labels. In the existing literature, consistency …
construct powerful models with limited labels. In the existing literature, consistency …
Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation
For the past decades, simulation‐based likelihood‐free inference methods have enabled
researchers to address numerous population genetics problems. As the richness and …
researchers to address numerous population genetics problems. As the richness and …
Unsupervised CT metal artifact learning using attention-guided β-CycleGAN
Metal artifact reduction (MAR) is one of the most important research topics in computed
tomography (CT). With the advance of deep learning approaches for image reconstruction …
tomography (CT). With the advance of deep learning approaches for image reconstruction …
Mixed graph contrastive network for semi-supervised node classification
Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …
node classification in recent years. However, the problem of insufficient supervision …
Gdpp: Learning diverse generations using determinantal point processes
Generative models have proven to be an outstanding tool for representing high-dimensional
probability distributions and generating realistic looking images. An essential characteristic …
probability distributions and generating realistic looking images. An essential characteristic …
A simple baseline algorithm for graph classification
Graph classification has recently received a lot of attention from various fields of machine
learning eg kernel methods, sequential modeling or graph embedding. All these …
learning eg kernel methods, sequential modeling or graph embedding. All these …