MixUp

This is an implement and Improvement  on mixup: Beyond Empirical Risk Minimization https://arxiv.org/abs/1710.09412

The improvement

1. add backward
2. add mix rate

Two scenes:

The detail design of MixUp layer:

The results:

The symbol of resnet50 is writen by mxnet https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/symbols, there have many versions. And i havenot do any optimizion for it. All the results are based on this baseline.

cifar10	alpha	mix_rate	test Acc	initial learning rate	batch size
(ERM)resnet50 90epoch	-	-	0.87900390625	0.05	256
(ERM)resnet50 200epoch	-	-	0.89365234375	0.05	256
(ERM)resnet50 300epoch	-	-	0.8931640625	0.05	256
(mixup)resnet50 90epoch	0.2	0.7	0.8609375	0.7	256
(mixup)resnet50 200epoch	0.2	0.7	0.91611328125	0.7	256
(mixup)resnet50 300epoch	0.2	0.7	0.9224609375	0.7	256
mixup in feature maps（resnet50 head conv）90epoch	0.2	0.7	0.8544921875	0.7	256
mixup in feature maps（resnet50 head conv）200epoch	0.2	0.7	0.91796875	0.7	256
mixup in feature maps（resnet50 head conv）300epoch	0.2	0.7	0.91845703125	0.7	256

MixUp

Mixup in feature map （resnet50 head conv）

ERM

Usage

install mxnet0.12 The mixup is in:symbols/mixup.py you can use it in your codes like:

data ,label = mx.sym.Custom(data= data,label = label,alpha = 0.2,num_classes = num_classes,batch_size = batch_size,mix_rate =0.7,op_type = 'MixUp')

label is the vector like [4,8,...9]

download the dataset

http://data.mxnet.io/data/cifar10/cifar10_val.rec

http://data.mxnet.io/data/cifar10/cifar10_train.rec

train:

./train.sh

test:

./test.sh

Reference

@article{zhang2017mixup, title={mixup: Beyond Empirical Risk Minimization}, author={Zhang, Hongyi and Cisse, Moustapha and Dauphin, Yann N and Lopez-Paz, David}, journal={arXiv preprint arXiv:1710.09412}, year={2017} }