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Unofficial Pytorch Implementation Of AdversarialAutoAugment(ICLR2020)

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Adversarial-Autoaugment-Pytorch

Unofficial Pytorch Implementation Of AdversarialAutoAugment(ICLR2020)

Current Issue

I want some help from those who know how to solve these issues.

  • Can not reproduce paper's results

  • Adversarial Collapsing : See /Examples/Analysis.ipynb

Quick Start

# Training with DistributedDataParallel
$ python -m torch.distributed.launch --nproc_per_node ${NUM_GPUS} main.py \
    --load_conf ${conf_dir} \
    --logdir './logs' \
    --M 8 \ 
    --seed 0 \
    -- amp \ 
    >> output.log

# Training with DataParallel
$ python main.py \
    --load_conf ${conf_dir} \
    --logdir './logs' \
    --M 8 \ 
    --seed 0 \
    -- amp \
    >> output.log

# Evaluate
$ python evaluate.py \
    --load_conf ${conf_dir} \
    --logdir './logs' \
    --seed 0 

Results

CIFAR-10

Model(CIFAR-10) Paper
(direct/transfer)
Ours
Wide-ResNet-28-10 1.90±0.15 / 2.45±0.13 2.35 / - Download
Shake-Shake(26 2x32d) 2.36±0.10 / - 2.51 / - Download
Shake-Shake(26 2x96d) 1.85±0.12 / - 2.43 / - Download
Shake-Shake(26 2x112d) 1.78±0.05 / - - Download
PyramidNet+ShakeDrop 1.36±0.06 / - - Download

CIFAR-100

Model(CIFAR-100) Paper
(direct/transfer)
Ours
Wide-ResNet-28-10 15.49±0.18 / 16.48±0.15 - Download
Shake-Shake(26 2x96d) 14.10±0.15 / - - Download
PyramidNet+ShakeDrop 10.42±0.20 / - - Download

ImageNet

Model(ImageNet) Paper
(top1/top5/ transfer_top1)
Ours
Resnet50 20.60±0.15 / 5.53±0.05 / - - Download
Resnet50D 20.00±0.12 / 5.25±0.03 / 20.20±0.05 - Download
Resnet200 18.68±0.18 / 4.70±0.05 / 19.05±0.10 - Download

CIFAR-10-C

Model(CIFAR-10-C) Augmix w/ JSD Adv AA
Wide-Resnet-40-2 11.2 - Download
Wide-Resnet-28-10 - 10.41 Download
Shake-Shake(26 2x32d) - 16.69 Download

Different From The Paper

  • I did not include SamplePairing -> NUM_OPS = 15 (16 in the paper)
  • Borrow unknown hyperparameter settings from fast-autoaugment

TODO

Observations

References & Open Sources

ENAS

FastAutoAugment

Augmix

OpenReview of AdvAA