This is an implement and Improvement on mixup: Beyond Empirical Risk Minimization https://arxiv.org/abs/1710.09412
- add backward
- add mix rate
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]
http://data.mxnet.io/data/cifar10/cifar10_val.rec
http://data.mxnet.io/data/cifar10/cifar10_train.rec
./train.sh
./test.shZhang H, Cisse M, Dauphin Y N, et al. mixup: Beyond Empirical Risk Minimization[J]. arXiv preprint arXiv:1710.09412, 2017.