A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (1905.13294v3)

Abstract: Deep learning has solved a problem that as little as five years ago was thought by many to be intractable - the automatic recognition of patterns in data; and it can do so with accuracy that often surpasses human beings. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners trying to make sense out of the flood of data that now inundates our society. As public awareness of the efficacy of DL increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge produced by experts in the field. Where does one start? How does one determine if a particular model is applicable to their problem? How does one train and deploy such a network? A primer on the subject can be a good place to start. With that in mind, we present an overview of some of the key multilayer ANNs that comprise DL. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is becoming critical to many computer applications, we include a section on using neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where DL has emerged as a game-changing technology: anomalous behavior detection in financial applications or in financial time-series forecasting, predictive and prescriptive analytics, medical image processing and analysis and power systems research. The thrust of this review is to outline emerging areas of application-oriented research within the DL community as well as to provide a reference to researchers seeking to use it in their work for what it does best: statistical pattern recognition with unparalleled learning capacity with the ability to scale with information.

Overview of "A Review of Deep Learning with Special Emphasis on Architectures, Applications, and Recent Trends"

The paper under review, titled "A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends," offers an extensive survey of deep learning methodologies, their architectural advancements, and their application modalities. It methodically examines the various deep learning models, with a particular focus on elucidating the evolution of architectures, application domains, and emerging trends, thus serving as a comprehensive guide for researchers looking to delve into the domain of deep learning.

Key Architectural Insights

The paper first provides a foundational overview of Artificial Neural Networks (ANNs), laying the groundwork for understanding deep learning architectures. Among these architectures, the paper explores various models:

1. Deep Feed-Forward Networks: Serving as the foundational architecture, these networks facilitate complex hierarchical learning but are prone to overfitting and vanishing gradient problems. Recent developments include better regularization techniques and robust optimization algorithms.
2. Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs): RBMs are highlighted for their generative capabilities, playing a pivotal role in learning feature representations for deep networks. DBNs leverage stacked RBMs for efficient training, highlighting a significant step in deep learning's evolution.
3. Autoencoders: Known for dimensionality reduction and feature learning, they have seen variations like sparse and denoising autoencoders that improve robustness and feature representation.
4. Convolutional Neural Networks (CNNs): Explored for their proficiency in image-related tasks, CNNs represent a leap in dealing with spatial hierarchies in data, with architectures such as AlexNet marking a significant milestone in deep learning history.
5. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks: These networks excel in sequence data modeling, with LSTMs addressing RNN's vanishing gradient problem.
6. Generative Adversarial Networks (GANs): GANs represent an innovative approach in generative modeling, focusing on adversarial training to create robust generative models.

The discussion covers the evolution of these architectures and underscores how they cater to the specific needs dictated by the characteristics of data and application domain requirements.

Applications and Trends

The paper transitions into exploring profound applications of these architectures, emphasizing their competencies in various fields:

• Fraud Detection in Financial Services: Utilizes deep learning to detect anomalies and novel fraud types, leveraging unsupervised learning models like autoencoders and GANs.
• Financial Time Series Forecasting: Studies leveraging deep learning underscore its potential to model nonlinear dependencies, often outperforming traditional statistical methods in stock and forex market predictions.
• Prognostics and Health Management: Describes the role of deep learning in predicting the health and remaining useful life of systems, with recurrent models capturing temporal dependencies for accurate prognostics.
• Medical Image Processing: CNNs and other architectures have excelled in diagnostic tasks, from image classification to segmentation in medical settings, pushing the boundaries of automated diagnostics and analysis.
• Power Systems: From load forecasting to renewable energy output prediction, deep learning methods offer enhanced accuracy and robustness over conventional methods, demonstrating their practical utility in energy management.

Implications and Future Directions

The paper acknowledges the escalating role of deep learning across various industries, facilitated by advancements in hardware and algorithms. The future trajectory is poised towards developing models that require less data, increased robustness against adversarial inputs, and embracing unsupervised learning for self-directed progression. Additionally, the integration of reinforcement learning with deep learning is speculated to advance decision-making processes, compensating for the current limitations of data dependency.

In conclusion, the paper serves as a meticulous examination of deep learning's trajectory, adeptly capturing its architectural evolution, application diversity, and ongoing advancements, thus guiding researchers and practitioners in formulating informed research directions and application strategies within the expansive landscape of deep learning.