Awesome

MixUp

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

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

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]

./train.sh
./test.sh

Zhang H, Cisse M, Dauphin Y N, et al. mixup: Beyond Empirical Risk Minimization[J]. arXiv preprint arXiv:1710.09412, 2017.