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Adaboost Teacher (on Cifar-10)

Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation

The method is designed for domain adaptation segmentation (https://github.com/layumi/AdaBoost_Seg). It also can be applied to the semi-supervised setting on Cifar-10.

Mean Teacher Baseline

python -m experiments.cifar10_test

Ours

Here we do not change the original weight averaging setting in MeanTeacher, but add the adaptive sampler (every 2 epochs update sampler once).

python -m experiments.ada_cifar10_alpha2

Results

We run all experiments 10 times for a fair comparison. We only have 3 GPU on one machine, so we re-run the code. It is slightly different with the paper.

CIFAR-10 using 4000 labelstest error
CT-GAN [paper]9.98 ± 0.21
Mean Teacher ResNet-266.28 ± 0.15
Mean Teacher ResNet-26 (Our Re-implementation with 3 GPU)6.14 ± 0.24
Adaboost Teacher ResNet-26 (Our Re-implementation with 3 GPU)6.05 ± 0.12
All labels, state of the art [paper]2.86

Acknowledge

This code is largely borrowed from Mean Teacher for a fair comparison. We really appreciate the author to make the implementation open-source!