Jongchan / Unsupervised_data_augmentation_pytorch
A PyTorch implementation for Unsupervised Data Augmentation
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Unsupervised Data Augmentation (UDA)
A PyTorch implementation for Unsupervised Data Augmentation.
Disclaimer
- This is not an official implementation. The official TensorFlow implementation is at this Github link.
- Plan to implement CIFAR10 and ImageNet experiments.
Updates
- 2019.06.28: CIFAR-10 with 4,000 labeled set achieves top-1 accuracy 93.69% without TSA. (on paper, 94.33% without TSA)
Performance
CIFAR-10
| Exp | Top-1 acc(%) in paper | Top-1 acc(%) |
|---|---|---|
| Baseline | 79.74 | 83.94 |
| UDA (without TSA) | 94.33 | 93.69 |
| UDA | 94.90 | - |
ImageNet (10% labeled)
| Exp | Top-1 (paper) | Top-5 (paper) | Top-1 | Top-5 |
|---|---|---|---|---|
| RN50 | 55.09 | 77.26 (80.43 in S4L) | 54.184 | 79.116 |
| RN18 | - | - | 50.594 | 76.138 |
| UDA(RN50) | 68.66 | 88.52 | - | - |
| S4L(RN50) | - | 91.23 (ResNet50v2 4x) | - | - |
TODO List
- [x] CIFAR-10 baseline & UDA validation
- [x] ImageNet ResNet50 baseline validation
- [ ] ImageNet ResNet50 UDA validation
MISC
- CIFAR10 baseline on paper is from Realistic Evaluation of Deep Semi-Supervised Learning Algorithms, and it may be sub-optimal OR use different data split from the UDA paper. A naive baseline with weight decay 5e-4 and 100K iteration with cosine annealing LR can achieve higher performance as shown in the table.
- CIFAR10 labeled set is from AutoAugment policy search subset.
- CIFAR10 AutoAugment policy includes full set (95 policies), rather than 25 policies.
- ImageNet labeled set is randomly selected 10% for each class.
- ImageNet baseline settings are from S4L: Self-Supervised Semi-Supervised Learning.
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