Several SSL methods (Pi model, Mean Teacher) are implemented in pytorch
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README.md

ssl (semi-supervised learning)

This repository contains code to reproduce "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms" in pytorch. Currently, only supervised baseline, PI-model[2] and Mean-Teacher[3] are implemented. We attempted to follow the description in the paper, but there are several differences made intentionally. There may be other differences made accidentally from experiments in the paper.

  • The training code is under modification.

Prerequisites

Tested on

  • python 2.7
  • pytorch 0.4.0

Download ZCA preprocessed CIFAR-10 dataset

  • As described in the paper, global contrast normalize (GCN) and ZCA are important steps for the performance. We preprocess CIFAR-10 dataset using the code implemented in Mean-Teacher repository. The code is in tensorflow/dataset folder. Place the preprocessed file (e.g. cifar10_gcn_zca_v2.npz) into a subfolder (e.g. cifar10_zca).

Experiment detail

To Run

For basline

python train.py -a=wideresnet -m=baseline -o=adam -b=225 --dataset=cifar10_zca --gpu=0,1 --lr=0.003 --boundary=0

For Pi model

python train.py -a=wideresnet -m=pi -o=adam -b=225 --dataset=cifar10_zca --gpu=0,1 --lr=0.0003 --boundary=0

For Mean Teacher

python train.py -a=wideresnet -m=mt -o=adam -b=225 --dataset=cifar10_zca --gpu=0,1 --lr=0.0004 --boundary=0
  • boundary option is for different label/unlabel division [0, 9].

You can check the average error rates for n runs using check_result.py. For example, you trained baseline model on 10 different boundary,

python check_result.py --fdir ckpt_cifar10_zca_wideresnet_baseline_adam_e1200/ --fname wideresnet --nckpt 10 

Result (CIFAR-10)

Method WideResnet28x2 [1] WideResnet28x3 w/ dropout (ours)
Supervised 20.26 (0.38)
PI Model 16.37 (0.63)
Mean Teacher 15.87 (0.28)
VAT 13.86 (0.27) -
VAT + EM 13.13 (0.39) -

References

[1] Oliver, Avital, et al. "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms." arXiv preprint arXiv:1804.09170 (2018).

[2] Laine, Samuli, and Timo Aila. "Temporal ensembling for semi-supervised learning." arXiv preprint arXiv:1610.02242 (2016).

[3] Tarvainen, Antti, and Harri Valpola. "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results." Advances in neural information processing systems. 2017.

[4] https://github.com/CuriousAI/mean-teacher

[5] https://github.com/facebookresearch/odin