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"MutexMatch: Semi-Supervised Learning With Mutex-Based Consistency Regu" by Yue Duan, Zhen Zhao et al.

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this article, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict “what it is” by the conventional true-positive classifier (TPC), while low-confidence samples are employed to achieve a simpler goal to predict with ease “what it is not” by the true-negative classifier (TNC). In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by the consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., Canadian Institute for Advanced Research (CI ....

Canadian Institute For Advanced Research , Canadian Institute , Advanced Research , Data Models , Utex Based Consistency Regularization , Predictive Models , Semi Supervised Classification , Semisupervised Learning , Task Analysis ,

"Reducing Background Induced Domain Shift for Adaptive Person Re-Identi" by Jianjun Lei, Tianyi Qin et al.

Cross-domain person re-identification (Re-ID) is a challenging and important task in monitoring safety and procedure compliance of industrial work places. In this paper, a novel method is proposed to reduce background induced domain shift for adaptive person Re-ID. Specifically, a foreground-background joint clustering module is proposed to extract discriminative foreground and background features and an attention-based feature disentanglement module is designed to reduce the interference of background with the extraction of discriminative foreground features. Experimental results on three widely used person Re-ID benchmarking datasets (Market-1501, DukeMTMC-reID, and MSMT17) have demonstrated that the proposed method achieves promising performance compared with the state-of-the-art methods. ....

Adaptation Models , Domain Adaptation , Eature Disentanglement , Feature Extraction , Intelligent Surveillance , Person Re Identification , Task Analysis ,

"Defensive Few-shot Learning" by Wenbin Li, Lei Wang et al.

This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting. To address this situation, we develop a general defensive few-shot learning (DFSL) framework to answer the following two key questions: (1) how to transfer adversarial defense knowledge from one sample distribution to another? (2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting? To answer the first question, we propose an episode-based adversarial training mechanism by assuming a task-level distribution consistency to better transfer the adversarial defense knowledge. As for the second question, within each few-s ....

Adversarial Attacks , Convolutional Neural Networks , Efensive Few Shot Learning , Distribution Consistency , Pisodic Training , Graphics Processing Units , Image Classification , Learning Systems , Task Analysis ,

"Maximizing Sensing and Computation Rate in Ad-Hoc Energy Harvesting Io" by Hang Yu and Kwan Wu Chin

This paper considers collection and processing of data by solar-powered servers operating in an Internet of Things (IoT) network. Specifically, these servers aim to cooperatively maximize the amount of data collected from devices and computed over multiple time slots. To achieve this aim, they must consider computation deadline, time-varying energy arrivals at sensor devices and other servers. To this end, this paper outlines a mixed integer linear program (MILP), which can be used to optimize the sensing time of sensor devices, offloading decision of each server, and the number of virtual machines (VMs) assigned to each device. Further, this paper proposes a multi-agent co-operative Q-learning approach coupled with the Hungarian algorithm to assign VMs to devices. It allows servers to learn when to share their energy and VMs with neighbor servers using only non-causal energy and channel gain information. The simulation results show that the amount of processed data by co-operative Q-l ....

Ad Hoc Networks , Data Collection , Edge Computing , Nternet Of Things , Q Learning , Renewable Energy , Task Analysis , Wireless Communication , Wireless Sensor Networks ,

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