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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

Deep learning for monocular depth estimation: A review

Improving deep neural network with Multiple Parametric Exponential Linear Units

Robust regional bounding spherical descriptor for 3D face recognition and emotion analysis

High-level features can represent the semantics of the original data and it is a plausible way to avoid the problem of hand-crafted features for face recognition. This paper proposes an effective deep learning framework by stacking multiple output features that learned through each stage of the Convolutional Neural Network (CNN). Different from the traditional deep learning network, we use Principal Component Analysis (PCA) to get the filter kernels of convolutional layer, which is name as Stacked PCA Network (SPCANet). Our SPCANet model follows the basic architecture of the CNN, which comprises three layers in each stage: convolutional filter layer, nonlinear processing layer and feature pooling layer. Firstly, in the convolutional filter layer of our model, PCA instead of stochastic gradient descent (SGD) is employed to learn filter kernels, and the output of all cascaded convolutional filter layers is used as the input of nonlinear processing layer. Secondly, the following nonlinear processing layer is also simplified. We use hashing method for nonlinear processing. Thirdly, the block based histograms instead of max-pooling technique are employed in the feature pooling layer. In the last output layer, the output of each stage is stacked together as one final feature output of our model. Extensive ex- periments conducted on many different face recognition scenarios demonstrate the effectiveness of our proposed approach.

Stacked PCA Network (SPCANet): An effective deep learning for face recognition

This paper describes a 3D facial expression recognition approach based on distance and angle features, which can be got from the localized facial feature points. The probabilistic Neutral Network (PNN) architecture is used to classify the facial expressions based on BU-3DFE database. This paper adds the facial feature vectors with the information of slopes and the angles as the feature vectors got from the facial feature points, not only the distance information mentioned in the previous work. Thus it receives a better performance with an average recognition rate of 90.2%.

Yue Ming

Papers

Deep learning for monocular depth estimation: A review

Improving deep neural network with Multiple Parametric Exponential Linear Units

Robust regional bounding spherical descriptor for 3D face recognition and emotion analysis

Stacked PCA Network (SPCANet): An effective deep learning for face recognition

3D Facial expression recognition based on basic geometric features