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.