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DWD

This is the official implementation code for DWD: Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning (NeurIPS 2024')

To reproduce the result, please following the procedure.

Environment configuration.

  • download the assets from here and place the files in proper location.
  • 'Cifar100.zip, cifar10.zip' locate in '/data' and extract them
  • 'Autoencoder_KL_Cifars_16x16x3_Attn.pt' place in '/assets/autoencoder'
  • 'train_1000labels.npz' place in 'assets/fid_stats'

Train DWD

  • python train_ldm_DWD_SDE.py --interval --cfg True --rep_with_img True --cond joint --exp_name Reproducing

Transform the data based on unlabeled data

  • python sample_ldm_SDE.py --pseudo True --cond multi

Train DWD-SL using transformed data

  • python downstream_cifars.py --init Random --lr 0.03 --lr-decay True --wd 1e-4 --bs 64 --nest True --optim AdamW --arch wideresnet --epochs 256 --steps 1024 --rand_aug True --label-smoothing 0.15 --seed 74 --exp_name AdamW_003lr_Seed74

Train FixMatch + DWD-UT using tranformed data

  • python train.py --dataset cifar10 --num-labeled 100 --arch wideresnet --batch-size 64 --lr 0.03 --expand-labels --seed 5 --u_data ood --img_size 32 --exp_name Reproducing

Quantitative Result

[ DWD-SL ] Uses transformed data with given class conditions ( Supervised )
[ DWD-UT ] Utilizes only transformed unlabeled data ( Semi-supervised )


  • Performance comparison on four tasks. We report the mean accuracy averaged over three seeds, along with standard error. Top scores for each task are highlighted
Task Name MixMatch FixMatch MPL OpenMatch Fix-A-Step IOMatch DWD-SL
SixAnimal (ζ = 75%) 80.77±0.11 82.50±0.16 65.62±0.47 80.34±0.21 85.34±0.17 83.05±0.16 85.86±0.28
CIFAR-10/100 71.02±0.32 78.91±0.15 70.95±0.34 70.15±0.30 74.60±0.31 77.66±0.22 80.05±0.14
ImageNet-30 68.67±0.37 70.07±0.26 72.65±0.70 72.78±0.48 79.67±0.81 79.23±0.29 82.20±0.38
ImageNet-100 69.30±0.41 65.11±0.32 68.43±0.33 65.42±0.36 65.80±0.49 66.85±0.19 82.81±0.31

  • Performance of standard SSL methods before and after applying DWD-UT.
Task Name MixMatch MixMatch
+ DWD-UT		 FixMatch FixMatch
+ DWD-UT		 MPL MPL
+ DWD-UT
SixAnimal (ζ = 75%) 80.77±0.11 84.72±0.22 82.50±0.16 87.17±0.19 65.62±0.47 83.88±0.18
CIFAR-10/100 71.02±0.32 80.47±0.49 78.91±0.15 83.80±0.25 70.95±0.34 80.24±0.56
ImageNet-30 68.67±0.37 85.20±0.10 70.07±0.26 81.87±0.61 72.65±0.70 90.20±0.23
ImageNet-100 69.30±0.41 81.62±0.36 65.11±0.32 80.38±0.34 68.43±0.33 75.66±0.26

  • Performance of open-set SSL methods before and after applying DWD-UT.
Task Name OpenMatch OpenMatch
+ DWD-UT		 Fix-A-Step Fix-A-Step
+ DWD-UT		 IOMatch IOMatch
+ DWD-UT
SixAnimal (ζ = 75%) 80.34±0.21 85.71±0.33 85.34±0.17 86.68±0.23 83.05±0.16 87.20±0.13
CIFAR-10/100 70.15±0.30 80.99±0.03 74.60±0.31 79.02±0.75 77.66±0.22 83.22±0.16
ImageNet-30 72.78±0.48 75.28±0.68 79.67±0.81 82.95±0.45 79.23±0.29 81.96±0.26
ImageNet-100 65.42±0.36 80.02±0.45 65.80±0.49 76.23±0.37 66.85±0.19 80.19±0.13

Qualitative Result

  • Data transform example by DWD poster

  • Comparison of feature distance between origin and transformed images poster

Our code implementation is based on the following public repositories:

  1. Latent Diffusion: https://github.com/CompVis/latent-diffusion
  2. DPT: https://github.com/ML-GSAI/DPT
  3. nnPU: https://github.com/kiryor/nnPUlearning
  4. DPM-Solver: https://github.com/LuChengTHU/dpm-solver
  5. CFG: https://github.com/coderpiaobozhe/classifier-free-diffusion-guidance-Pytorch/tree/master