Recent advances and clinical applications of deep learning in medical image analysis

X Chen, X Wang, K Zhang, KM Fung, TC Thai… - Medical image …, 2022‏ - Elsevier
Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …

A survey on data‐efficient algorithms in big data era

A Adadi - Journal of Big Data, 2021‏ - Springer
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Multi-concept customization of text-to-image diffusion

N Kumari, B Zhang, R Zhang… - Proceedings of the …, 2023‏ - openaccess.thecvf.com
While generative models produce high-quality images of concepts learned from a large-
scale database, a user often wishes to synthesize instantiations of their own concepts (for …

Ablating concepts in text-to-image diffusion models

N Kumari, B Zhang, SY Wang… - Proceedings of the …, 2023‏ - openaccess.thecvf.com
Large-scale text-to-image diffusion models can generate high-fidelity images with powerful
compositional ability. However, these models are typically trained on an enormous amount …

Stylegan-nada: Clip-guided domain adaptation of image generators

R Gal, O Patashnik, H Maron, AH Bermano… - ACM Transactions on …, 2022‏ - dl.acm.org
Can a generative model be trained to produce images from a specific domain, guided only
by a text prompt, without seeing any image? In other words: can an image generator be …

Generative neural articulated radiance fields

A Bergman, P Kellnhofer, W Yifan… - Advances in …, 2022‏ - proceedings.neurips.cc
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only
collections of single-view 2D photographs has very recently made much progress. These 3D …

Training generative adversarial networks with limited data

T Karras, M Aittala, J Hellsten, S Laine… - Advances in neural …, 2020‏ - proceedings.neurips.cc
Training generative adversarial networks (GAN) using too little data typically leads to
discriminator overfitting, causing training to diverge. We propose an adaptive discriminator …

Gan prior embedded network for blind face restoration in the wild

T Yang, P Ren, X **e, L Zhang - Proceedings of the IEEE …, 2021‏ - openaccess.thecvf.com
Blind face restoration (BFR) from severely degraded face images in the wild is a very
challenging problem. Due to the high illness of the problem and the complex unknown …

Differentiable augmentation for data-efficient gan training

S Zhao, Z Liu, J Lin, JY Zhu… - Advances in neural …, 2020‏ - proceedings.neurips.cc
The performance of generative adversarial networks (GANs) heavily deteriorates given a
limited amount of training data. This is mainly because the discriminatorsis memorizing the …

Image-to-image translation: Methods and applications

Y Pang, J Lin, T Qin, Z Chen - IEEE Transactions on Multimedia, 2021‏ - ieeexplore.ieee.org
Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …