This is a fork of the FixMatch-Pytorch repo from https://github.com/kekmodel/FixMatch-pytorch
#Labels per class | 10 | 40 | 400 |
---|---|---|---|
Thanh's trials | 92.37 | 94.03 | 95.09 |
Weights and log files are in vision38:/data/tvu/classes/fixMatch-pytorch/results/
Realisticity Analysis
- Hypothesis 1: strongly and weakly augmented images have different realisiticity and we can measure/distinct this using out-of-distribution methods
- Hypothesis 2: there are variation of realisiticity even among strongly augmented images. e.g. we expect posterization to be more unrealisitic than flip+translate+crop
Improving SSL
- Hypothesis 3: we can improve SSL by training with appropriate/various levels of unrealisticity of strong augmentation
- Test run this repo with CIFAR-10
Realisticity Analysis
- Integrate OOD detection for measuring "realisiticity" of augmented images e.g. https://arxiv.org/abs/1912.03263 https://arxiv.org/abs/1905.11001
- Manually check ODD score and qualitative realisticity of sample augmented images. See save-aug branch for the extraction sample augmented images (before normalization)
- Generate/save all augmented variation as use in one epoch of FixMatch
- Analysize the ODD scores of this distribution augmented images to test hypotheses 1 and 2
Improving SSL
- Train FixMatch with various levels of unrealistic augmentation
(below is the README from the original repo)
This is an unofficial PyTorch implementation of FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. The official Tensorflow implementation is here.
This code is only available in FixMatch (RandAugment). Now only experiments on CIFAR-10 and CIFAR-100 are available.
- Python 3.6+
- PyTorch 1.4
- torchvision 0.5
- tensorboard
- tqdm
- numpy
- apex (optional)
Train the model by 4000 labeled data of CIFAR-10 dataset:
python train.py --dataset cifar10 --num-labeled 4000 --arch wideresnet --batch-size 64 --lr 0.03 --seed 5 --out results/[email protected]
Train the model by 10000 labeled data of CIFAR-100 dataset by using DistributedDataParallel:
python -m torch.distributed.launch --nproc_per_node 4 ./train.py --dataset cifar100 --num-labeled 10000 --arch wideresnet --batch-size 16 --lr 0.03 --out results/cifar100@10000
* When using DDP, do not use a seed.
tensorboard --logdir=<your out_dir>
#Labels | 40 | 250 | 4000 |
---|---|---|---|
Paper (RA) | 86.19 ± 3.37 | 94.93 ± 0.65 | 95.74 ± 0.05 |
This code | 92.92 | 94.13 | 95.33 |
Acc. curve | link | link | link |
#Labels | 400 | 2500 | 10000 |
---|---|---|---|
Paper (RA) | 51.15 ± 1.75 | 71.71 ± 0.11 | 77.40 ± 0.12 |
This code | - | - | - |
Acc. curve | - | - | - |
@article{sohn2020fixmatch,
title={FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence},
author={Kihyuk Sohn and David Berthelot and Chun-Liang Li and Zizhao Zhang and Nicholas Carlini and Ekin D. Cubuk and Alex Kurakin and Han Zhang and Colin Raffel},
journal={arXiv preprint arXiv:2001.07685},
year={2020},
}