[HTML][HTML] A state-of-the-art survey on deep learning theory and architectures
In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …
domains. This new field of machine learning has been growing rapidly and has been …
The history began from alexnet: A comprehensive survey on deep learning approaches
Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …
the past few years. This new field of machine learning has been growing rapidly and applied …
Towards real-world blind face restoration with generative facial prior
Blind face restoration usually relies on facial priors, such as facial geometry prior or
reference prior, to restore realistic and faithful details. However, very low-quality inputs …
reference prior, to restore realistic and faithful details. However, very low-quality inputs …
Deep learning for image super-resolution: A survey
Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …
enhance the resolution of images and videos in computer vision. Recent years have …
Attention, please! A survey of neural attention models in deep learning
A de Santana Correia, EL Colombini - Artificial Intelligence Review, 2022 - Springer
In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …
limited ability to process competing sources, attention mechanisms select, modulate, and …
Dr2: Diffusion-based robust degradation remover for blind face restoration
Blind face restoration usually synthesizes degraded low-quality data with a pre-defined
degradation model for training, while more complex cases could happen in the real world …
degradation model for training, while more complex cases could happen in the real world …
To learn image super-resolution, use a gan to learn how to do image degradation first
This paper is on image and face super-resolution. The vast majority of prior work for this
problem focus on how to increase the resolution of low-resolution images which are …
problem focus on how to increase the resolution of low-resolution images which are …
Fsrnet: End-to-end learning face super-resolution with facial priors
Abstract Face Super-Resolution (SR) is a domain-specific superresolution problem. The
facial prior knowledge can be leveraged to better super-resolve face images. We present a …
facial prior knowledge can be leveraged to better super-resolve face images. We present a …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Spatial-frequency mutual learning for face super-resolution
Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the
low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved …
low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved …