2 research outputs found
Survey on Deep Neural Networks in Speech and Vision Systems
This survey presents a review of state-of-the-art deep neural network
architectures, algorithms, and systems in vision and speech applications.
Recent advances in deep artificial neural network algorithms and architectures
have spurred rapid innovation and development of intelligent vision and speech
systems. With availability of vast amounts of sensor data and cloud computing
for processing and training of deep neural networks, and with increased
sophistication in mobile and embedded technology, the next-generation
intelligent systems are poised to revolutionize personal and commercial
computing. This survey begins by providing background and evolution of some of
the most successful deep learning models for intelligent vision and speech
systems to date. An overview of large-scale industrial research and development
efforts is provided to emphasize future trends and prospects of intelligent
vision and speech systems. Robust and efficient intelligent systems demand
low-latency and high fidelity in resource-constrained hardware platforms such
as mobile devices, robots, and automobiles. Therefore, this survey also
provides a summary of key challenges and recent successes in running deep
neural networks on hardware-restricted platforms, i.e. within limited memory,
battery life, and processing capabilities. Finally, emerging applications of
vision and speech across disciplines such as affective computing, intelligent
transportation, and precision medicine are discussed. To our knowledge, this
paper provides one of the most comprehensive surveys on the latest developments
in intelligent vision and speech applications from the perspectives of both
software and hardware systems. Many of these emerging technologies using deep
neural networks show tremendous promise to revolutionize research and
development for future vision and speech systems
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 in most of the application domains with some new
modalities of applications, which helps to open new opportunity. There are
different methods have been proposed on different category of learning
approaches, which includes supervised, semi-supervised and un-supervised
learning. The experimental results show state-of-the-art performance of deep
learning over traditional machine learning approaches in the field of Image
Processing, Computer Vision, Speech Recognition, Machine Translation, Art,
Medical imaging, Medical information processing, Robotics and control,
Bio-informatics, Natural Language Processing (NLP), Cyber security, and many
more. This report presents a brief survey on development of DL approaches,
including Deep Neural Network (DNN), 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). In
addition, we have included recent development of proposed advanced variant DL
techniques based on the mentioned DL approaches. Furthermore, DL approaches
have explored and evaluated in different application domains are also included
in this survey. We have also comprised recently developed frameworks, SDKs, and
benchmark datasets that are used for implementing and evaluating deep learning
approaches. There are some surveys have published on Deep Learning in Neural
Networks [1, 38] and a survey on RL [234]. However, those papers have not
discussed the individual advanced techniques for training large scale deep
learning models and the recently developed method of generative models [1].Comment: 39 pages, 46 figures, 3 tables. arXiv admin note: text overlap with
arXiv:1408.3264, arXiv:1411.404