Incremental Class Learning Approach and Its Applications to

Abstract Incremental Class Learning (ICL) provides a feasible framework for the development of scalable learning systems. Instead of learning a complex problem at once, ICL focuses on learning subproblems incrementally, one at a time -using the results of prior learning for subsequent learning -and then combining the solutions in an appropriate manner. With respect to multi-class classification problems, the ICL approach presented in this paper can be summarized as follows. Initially the system focuses on one category. After it learns this category, it tries to identify a compact subset of features (nodes) in the hidden layers, that are crucial for the recognition of this category. The system then freezes these crucial nodes (features) by fixing their incoming weights. As a result, these features cannot be obliterated in subsequent learning. These frozen features are available during subsequent learning and can serve as parts of weight structures built to recognize other categories. As more categories are learned, the set of features gradually stabilizes and learning a new category requires less effort. Eventually, learning a new category may only involve combining existing features in an appropriate manner. The approach promotes the sharing of learned features among a number of categories and also alleviates the wellknown catastrophic interference problem. We present promising results of applying the ICL approach to the unconstrained Handwritten Digit Recognition problem, based on a spatio-temporal representation of patterns

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference

Semantic radical consistency and character transparency effects in Chinese: an ERP study

BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin

A Concept for Deployment and Evaluation of Unsupervised Domain Adaptation in Cognitive Perception Systems

Jüngste Entwicklungen im Bereich des tiefen Lernens ermöglichen Perzeptionssystemen datengetrieben Wissen über einen vordefinierten Betriebsbereich, eine sogenannte Domäne, zu gewinnen. Diese Verfahren des überwachten Lernens werden durch das Aufkommen groß angelegter annotierter Datensätze und immer leistungsfähigerer Prozessoren vorangetrieben und zeigen unübertroffene Performanz bei Perzeptionsaufgaben in einer Vielzahl von Anwendungsbereichen.Jedoch sind überwacht-trainierte neuronale Netze durch die Menge an verfügbaren annotierten Daten limitiert und dies wiederum findet in einem begrenzten Betriebsbereich Ausdruck. Dabei beruht überwachtes Lernen stark auf manuell durchzuführender Datenannotation. Insbesondere durch die ständig steigende Verfügbarkeit von nicht annotierten großen Datenmengen ist der Gebrauch von unüberwachter Domänenanpassung entscheidend. Verfahren zur unüberwachten Domänenanpassung sind meist nicht geeignet, um eine notwendige Inbetriebnahme des neuronalen Netzes in einer zusätzlichen Domäne zu gewährleisten. Darüber hinaus sind vorhandene Metriken häufig unzureichend für eine auf die Anwendung der domänenangepassten neuronalen Netzen ausgerichtete Validierung. Der Hauptbeitrag der vorliegenden Dissertation besteht aus neuen Konzepten zur unüberwachten Domänenanpassung. Basierend auf einer Kategorisierung von Domänenübergängen und a priori verfügbaren Wissensrepräsentationen durch ein überwacht-trainiertes neuronales Netz wird eine unüberwachte Domänenanpassung auf nicht annotierten Daten ermöglicht. Um die kontinuierliche Bereitstellung von neuronalen Netzen für die Anwendung in der Perzeption zu adressieren, wurden neuartige Verfahren speziell für die unüberwachte Erweiterung des Betriebsbereichs eines neuronalen Netzes entwickelt. Beispielhafte Anwendungsfälle des Fahrzeugsehens zeigen, wie die neuartigen Verfahren kombiniert mit neu entwickelten Metriken zur kontinuierlichen Inbetriebnahme von neuronalen Netzen auf nicht annotierten Daten beitragen. Außerdem werden die Implementierungen aller entwickelten Verfahren und Algorithmen dargestellt und öffentlich zugänglich gemacht. Insbesondere wurden die neuartigen Verfahren erfolgreich auf die unüberwachte Domänenanpassung, ausgehend von der Tag- auf die Nachtobjekterkennung im Bereich des Fahrzeugsehens angewendet

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Efficient Learning Machines

Computer scienc

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition