Domain-Adversarial Training of Neural Networks

We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.Comment: Published in JMLR: http://jmlr.org/papers/v17/15-239.htm

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Dynamic Switching State Systems for Visual Tracking

This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together

Dynamic Switching State Systems for Visual Tracking

This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together

Efficient Methods for the Design and Training of Neural Networks

The field of artificial intelligence has seen significant advancements with the development of neural networks, which have numerous applications in computer vision, natural language processing, and speech processing. Despite these advancements, designing and training these networks still pose numerous challenges. This thesis aims to address two critical aspects of neural network development, design and training, within the context of computer vision tasks. The thesis focuses on three main challenges in the development of neural networks. The first challenge is finding an efficient way to perform architecture search in an extremely large or even unlimited search space. To address this challenge, the thesis proposes a Neural Search-space Evolution (NSE) scheme that enables efficient and effective architecture search in large-scale search spaces. The second challenge is to improve the efficiency of self-supervised learning for model pretraining. To address this challenge, the thesis proposes a combinatorial patches approach that significantly improves the efficiency of self-supervised learning. The third challenge is to develop an efficient and versatile multitask model that can leverage the benefits of large-scale multitask training. To address this challenge, the thesis proposes a Unified model for Human-Centric Perceptions (UniHCP) as a simple and scalable solution for a human-centric perception system that unifies multiple human-centric tasks into a neat, efficient, and scalable model. The results of this thesis demonstrate the effectiveness of the proposed methods in improving the practicality and performance of neural network design and training. The NSE scheme, combinatorial patches approach, and UniHCP have been tested on a broad range of datasets, tasks, and settings, yielding impressive results. These findings affirm the efficacy of the proposed methods in enhancing the efficiency of the design and training process of neural networks

Applied Analysis and Synthesis of Complex Systems: Proceedings of the IIASA-Kyoto University Joint Seminar, June 28-29, 2004

This two-day seminar aimed at introducing the new development of the COE by Kyoto University to IIASA and discussing general modeling methodologies for complex systems consisting of many elements, mostly via nonlinear, large-scale interactions. We aimed at clarifying fundamental principles in complex phenomena as well as utilizing and synthesizing the knowledge derived out of them. The 21st Century COE (Center of Excellence) Program is an initiative by the Japanese Ministry of Education, Culture, Science and Technology (MEXT) to support universities establishing discipline-specific international centers for education and research, and to enhance the universities to be the world's apex of excellence with international competitiveness in the specific research areas. Our program of "Research and Education on Complex Functional Mechanical Systems" is successfully selected to be awarded the fund for carrying out new research and education as Centers of Excellence in the field of mechanical engineering in 2003 (five-year project), and is expected to lead Japanese research and education, and endeavor to be the top in the world. The program covers general backgrounds in diverse fields as well as a more in-depth grasp of specific branches such as complex system modeling and analysis of the problems including: nonlinear dynamics, micro-mesoscopic physics, turbulent transport phenomena, atmosphere-ocean systems, robots, human-system interactions, and behaviors of nano-composites and biomaterials. Fundamentals of those complex functional mechanical systems are macroscopic phenomena of complex systems consisting of microscopic elements, mostly via nonlinear, large-scale interactions, which typically present collective behavior such as self-organization, pattern formation, etc. Such phenomena can be observed or created in every aspect of modern technologies. Especially, we are focusing upon; turbulent transport phenomena in climate modeling, dynamical and chaotic behaviors in control systems and human-machine systems, and behaviors of mechanical materials with complex structures. As a partial attainment of this program, IIASA and Kyoto University have exchanged Consortia Agreement at the beginning of the program in 2003, and this seminar was held to introduce the outline of the COE program of Kyoto University to IIASA researchers and to deepen the shared understandings on novel complex system modeling and analysis, including novel climate modeling and carbonic cycle management, through joint academic activities by mechanical engineers and system engineers. In this seminar, we invited a distinguished researcher in Europe as a keynote speaker and our works attained so far in the project were be presented by the core members of the project as well as by the other contributing members who participated in the project. All IIASA research staff and participants of YSSP (Young Scientist Summer Program) were cordially invited to attend this seminar to discuss general modeling methodologies for complex systems

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others