state of the art result for machine learning problems

https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems

1. Classification

Research Paper	Datasets	Metric	Source Code	Year
Dynamic Routing Between Capsules	MNIST	Test Error: 0.25±0.005	PyTorch Tensorflow Keras Chainer	2017
High-Performance Neural Networks for Visual Object Classification	NORB	Test Error: 2.53 ± 0.40	NOT FOUND	2011
ShakeDrop regularization	CIFAR-10 CIFAR-100	Test Error: 2.31% Test Error: 12.19%	NOT FOUND	2017
Aggregated Residual Transformations for Deep Neural Networks	CIFAR-10	Test Error: 3.58%	PyTorch	2017
Dynamic Routing Between Capsules	MultiMNIST	Test Error: 5%	PyTorch Tensorflow Keras Chainer	2017
Squeeze-and-Excitation Networks	ImageNet-1k	Top-1 Error 18.68	CAFFE	2017
Aggregated Residual Transformations for Deep Neural Networks	ImageNet-1k	Top-1 Error: 20.4%	PyTorch	2016

2. Instance Segmentation

Research Paper	Datasets	Metric	Source Code	Year
Mask R-CNN	COCO	Average Precision: 37.1%	TensorFlow (This is not official. Performance is worse than the reported AP)	2017

来源：CSDN

作者：Bevision

链接：https://blog.csdn.net/u014333051/article/details/78518509