Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates.
Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead, significantly restricts their practical deployments on resource-limited devices.
In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.
The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy.
In this paper, we present a novel purified memory mechanism that simulates the recognition process of human beings.
no code implementations • 21 Jan 2021 • Zhenyi Zheng, Yue Zhang, Victor Lopez-Dominguez, Luis Sánchez-Tejerina, Jiacheng Shi, Xueqiang Feng, Lei Chen, Zilu Wang, Zhizhong Zhang, Kun Zhang, Bin Hong, Yong Xu, Youguang Zhang, Mario Carpentieri, Albert Fert, Giovanni Finocchio, Weisheng Zhao, Pedram Khalili Amiri
Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications.
Mesoscale and Nanoscale Physics