다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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 applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

A State-of-the-Art Survey on Deep Learning Theory and Architectures

A review on the attention mechanism of deep learning

https://ibug.doc.ic.ac.uk/media/uploads/documents/sandbach2012survey.pdf

Static and dynamic 3D facial expression recognition: A comprehensive survey

A survey on deep learning based face recognition

RFRN: A recurrent feature refinement network for accurate and efficient scene text detection

A new scheme for 3D face recognition is presented in this paper. Firstly, we use Iterative Closet Point (ICP) to align all 3D faces with the first 3D face. Secondly, we reduce noise, especially the noise which in front of the face, and remove the spikes. Then we detect the nose tip point. Once the nose tip is successfully found, we crop a region, which is defined by a sphere radius of 100 mm centered at the nose tip. Then we use the Corresponding Point Direction Measure (CPDM) to matching the 3D face with the gallery 3D faces and get the score. At the same time, we use the region to construct depth image, and get the Gabor feature, LBP feature, principle component of the depth image. Finally, we fuse the CPDM result, Gabor feature, LBP feature, and principle component of depth image to finish the recognition. This paper presents a new method for matching 3D face and a new scheme for 3D face recognition. Experiments demonstrated the efficiency and effectiveness of the new method.

3D Face recognition using Corresponding Point Direction Measure and depth local features

A Mandarin edutainment system integrated virtual learning environments

In this paper, a novel framework for 3D face recognition based on depth information, is proposed. The core of our framework is Spectral Regression Kernel Discriminate Analysis (SRKDA), a method for utilizing a reproducing kernel Hubert space (RKHS) into which data points are mapped. In order to overcome facial expression variation, we first utilize curvature information projected onto the moving least-squares (MLS) surface to segment a face rigid area, which is insensitive to expression variation. Then we make use of SRKDA to extract discrimination features for a depth image obtained by use of a 3D face mesh model, thus avoiding an eigen-decomposition of a kernel matrix. This effectively merges 3D facial shape information; then a nearest neighbor classifier is used for recognition. A non-linear kernel trick solves the high dimensional small sample size problem, and enhances feature extraction from the local non-linear structures of a face. Our experiments, using the CASIA 3D face database, show our framework performs more effectively and efficiently than many commonly used methods. SRKDA decreased the complexity from cubic-time to quadratic-time resulting in a very significant reduction in computation time. In addition, recognition accuracy, based on face rigid areas, improved accuracy significantly when compared to accuracy before segmentation.

Efficient Kernel Discriminate Spectral Regression for 3D face recognition

The present disclosure provides a three-dimensional facial recognition method and system. The method includes: performing pose estimation on an input binocular vision image pair by using a three-dimensional facial reference model, to obtain a pose parameter and a virtual image pair of the three-dimensional facial reference model with respect to the binocular vision image pair; reconstructing a facial depth image of the binocular vision image pair by using the virtual image pair as prior information; detecting, according to the pose parameter, a local grid scale-invariant feature descriptor corresponding to an interest point in the facial depth image; and generating a recognition result of the binocular vision image pair according to the detected local grid scale-invariant feature descriptor and training data having attached category annotations. The present disclosure can reduce computational costs and required storage space.

Yue Ming

Papers

RFRN: A recurrent feature refinement network for accurate and efficient scene text detection

3D Face recognition using Corresponding Point Direction Measure and depth local features

A Mandarin edutainment system integrated virtual learning environments

Efficient Kernel Discriminate Spectral Regression for 3D face recognition

Three-dimensional facial recognition method and system