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Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
A survey on data‐efficient algorithms in big data era
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 …
many application domains do not have access to big data because acquiring data involves a …
Multi-concept customization of text-to-image diffusion
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 …
scale database, a user often wishes to synthesize instantiations of their own concepts (for …
Ablating concepts in text-to-image diffusion models
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 …
compositional ability. However, these models are typically trained on an enormous amount …
Stylegan-nada: Clip-guided domain adaptation of image generators
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 …
by a text prompt, without seeing any image? In other words: can an image generator be …
Generative neural articulated radiance fields
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 …
collections of single-view 2D photographs has very recently made much progress. These 3D …
Training generative adversarial networks with limited data
Training generative adversarial networks (GAN) using too little data typically leads to
discriminator overfitting, causing training to diverge. We propose an adaptive discriminator …
discriminator overfitting, causing training to diverge. We propose an adaptive discriminator …
Gan prior embedded network for blind face restoration in the wild
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 …
challenging problem. Due to the high illness of the problem and the complex unknown …
Differentiable augmentation for data-efficient gan training
The performance of generative adversarial networks (GANs) heavily deteriorates given a
limited amount of training data. This is mainly because the discriminatorsis memorizing the …
limited amount of training data. This is mainly because the discriminatorsis memorizing the …
Image-to-image translation: Methods and applications
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 …
domain while preserving the content representations. I2I has drawn increasing attention and …