Group Normalization
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Code (22)
Tasks
Objectobject-detectionObject DetectionVideo ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Exploring the Efficacy of Group-Normalization in Deep Learning Models for Alzheimer's Disease Classification
Batch Normalization is an important approach to advancing deep learning since it allows multiple networks to train simultaneously. A problem arises when normalizing along the batch dimension because B.N.'s error increase…
On the Ideal Number of Groups for Isometric Gradient Propagation
Recently, various normalization layers have been proposed to stabilize the training of deep neural networks. Among them, group normalization is a generalization of layer normalization and instance normalization by allowi…
Fine-Grained Image ClassificationImage ClassificationObject DetectionPanoptic SegmentationGroup-wise normalization in differential abundance analysis of microbiome samples
A key challenge in differential abundance analysis of microbial samples is that the counts for each sample are compositional, resulting in biased comparisons of the absolute abundance across study groups. Normalization-b…
Generating configurations of increasing lattice size with machine learning and the inverse renormalization group
We review recent developments of machine learning algorithms pertinent to the inverse renormalization group, which was originally established as a generative numerical method by Ron-Swendsen-Brandt via the implementation…
A Lie Group Approach to Riemannian Batch Normalization
Manifold-valued measurements exist in numerous applications within computer vision and machine learning. Recent studies have extended Deep Neural Networks (DNNs) to manifolds, and concomitantly, normalization techniques …
Action RecognitionEEGTemporal Action Localization