Tensorflow implementation
Semi-Supervised Learning by Augmented Distribution Alignment Qin Wang, Wen Li, Luc Van Gool (ICCV 2019 Oral)
pip3 install tensorflow-gpu==1.13.1
pip3 install tensorpack==0.9.1
pip3 install scipy
cd convlarge
python3 cifar10.py --data_dir=./dataset/cifar10/ --dataset_seed=1
CUDA_VISIBLE_DEVICES=0 python3 train_cifar.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=./log/cifar10aug/ --num_epochs=2000 --epoch_decay_start=1500 --aug_flip=True --aug_trans=True --dataset_seed=1
CUDA_VISIBLE_DEVICES=0 python3 test_cifar.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=<path_to_log_dir> --dataset_seed=1
Here are the error rates we get using the above scripts :
Data Split Seed 1 | Seed 2 | Seed 3 | Reported |
---|---|---|---|
8.61% | 8.89% | 8.65% | 8.72+-0.12% |
Download our imagenet labeled/unlabeled split from this link, put them in ./resnet
cd resnet
python3 ./adanet-resnet.py --data <path_to_your_imagenet_files> -d 18 --mode resnet --batch 256 --gpu 0,1,2,3
- ConvLarge code is based on Takeru Miyato's tf implementation.
- ResNet code is based on Tensorpack's supervised imagenet training scripts.
MIT
@article{wang2019semi,
title={Semi-Supervised Learning by Augmented Distribution Alignment},
author={Wang, Qin and Li, Wen and Van Gool, Luc},
journal={arXiv preprint arXiv:1905.08171},
year={2019}
}
To reproduce Figure 4 in the paper, we provide the plot script and extracted features here. Notice that we use sklearn==0.20.1 for TSNE calculation.