In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discov...
In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.
Domain adaptive detection aims to improve the generality of a detector, learned from the labeled source domain, on the unlabeled target domain. In this work, drawing inspiration from the concept of stability from the ...
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In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. Th...
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ISBN:
(纸本)9781665454209
In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties.
This research paper presents a comprehensive exploration of various components to enhance the capabilities of a digital assistant tailored for visually impaired individuals. The first component explores how various im...
This research paper presents a comprehensive exploration of various components to enhance the capabilities of a digital assistant tailored for visually impaired individuals. The first component explores how various image captioning architectures and Vision Encoder Decoder models can be used for better environmental interaction. The second component concerns with facial expression recognition for better social interaction and the third component is a currency note identification component that aims to aid visually impaired individuals with cash transaction whilst the final component that explores a voice bot aims to improve a voice bot and how to manage accents, speech accuracy and naturality using modern deep learning techniques. By investigating these components, this research advances assistive technology, empowering visually impaired individuals with a conversational digital assistant that enhances environmental interaction, supports social interactions, and aids in currency identification.
With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitat...
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Among various technologies being applied for indoor localization, WiFi has become a common source of information to determine the pedestrian’s position due to the widespread of WiFi access points in indoor environmen...
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Context: software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches conce...
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With the deep integration of Internet technology and industrial development, traditional closed industrial Internet faces an increasing array of dynamic attack vectors. To address these evolving threats, dynamic defen...
With the deep integration of Internet technology and industrial development, traditional closed industrial Internet faces an increasing array of dynamic attack vectors. To address these evolving threats, dynamic defense methods have become a crucial component of industrial control network security. This paper proposes a dynamic defense method based on a shadow honeynet, which involves a collaborative construction strategy with the industrial control network to share operational states and network structures. The method aims to collect targeted attack traffic and utilizes an efficient adaptive clustering technique to classify malicious traffic within the shadow honeynet. This enables real-time adjustments of defense strategies to detect new attacks. Furthermore, extensive experiments are conducted on a composite dataset comprising real-world data, SCADA data, and industrial control honeypot data. The results demonstrate significant performance improvements of the proposed method compared to existing approaches.
In this paper, we briefly present classifier ensembles making use of nonlinear manifolds. Riemannian manifolds have been created using classifier interactions which are presented as symmetric and positive-definite (SP...
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