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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

  • CIFAR-10 baseline & UDA validation
  • 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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A PyTorch implementation for Unsupervised Data Augmentation

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