Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Towards understanding regularization in batch normalization
Batch Normalization (BN) improves both convergence and generalization in training neural
networks. This work understands these phenomena theoretically. We analyze BN by using a …
networks. This work understands these phenomena theoretically. We analyze BN by using a …
Differentiable learning-to-normalize via switchable normalization
We address a learning-to-normalize problem by proposing Switchable Normalization (SN),
which learns to select different normalizers for different normalization layers of a deep neural …
which learns to select different normalizers for different normalization layers of a deep neural …
Switchable whitening for deep representation learning
Normalization methods are essential components in convolutional neural networks (CNNs).
They either standardize or whiten data using statistics estimated in predefined sets of pixels …
They either standardize or whiten data using statistics estimated in predefined sets of pixels …
Smart nanoscopy: a review of computational approaches to achieve super-resolved optical microscopy
The field of optical nanoscopy, a paradigm referring to the recent cutting-edge developments
aimed at surpassing the widely acknowledged 200nm-diffraction limit in traditional optical …
aimed at surpassing the widely acknowledged 200nm-diffraction limit in traditional optical …
Momentum batch normalization for deep learning with small batch size
Normalization layers play an important role in deep network training. As one of the most
popular normalization techniques, batch normalization (BN) has shown its effectiveness in …
popular normalization techniques, batch normalization (BN) has shown its effectiveness in …
Differentiable dynamic normalization for learning deep representation
Abstract This work presents Dynamic Normalization (DN), which is able to learn arbitrary
normalization operations for different convolutional layers in a deep ConvNet. Unlike …
normalization operations for different convolutional layers in a deep ConvNet. Unlike …
Exemplar normalization for learning deep representation
Normalization techniques are important in different advanced neural networks and different
tasks. This work investigates a novel dynamic learning-to-normalize (L2N) problem by …
tasks. This work investigates a novel dynamic learning-to-normalize (L2N) problem by …
Scene Recognition of Remotely Sensed Images Based on Bayes Adjoint Batch Normalization
X YU, Z ZHENG, L MENG, L LI - Geomatics and Information Science …, 2023 - ch.whu.edu.cn
Objective: Normalization methods plays an important role in feature preprocessing phase
not only in conventional machine learning domain but also in contemporary deep learning …
not only in conventional machine learning domain but also in contemporary deep learning …
遥感影像场景识别的贝叶斯共轭批次归一化方法
虞欣， 郑肇葆， 孟令奎， 李林宜 - 武汉大学学报(信息科学版), 2023 - ch.whu.edu.cn
归一化(Normalization) 方法作为特征预处理的关键部分, 在浅学习和深度学习中都是至关重要
的. 针对批次归一化方法中存在对批次样本容量依赖较大的问题, 当前的优化思路主要是从样本 …
的. 针对批次归一化方法中存在对批次样本容量依赖较大的问题, 当前的优化思路主要是从样本 …