Label Hallucination for Few-Shot Classification

Related tags

Deep Learning LabelHalluc
Overview

Label Hallucination for Few-Shot Classification

This repo covers the implementation of the following paper: Label Hallucination for Few-Shot Classification . If you find this repo useful for your research, please consider citing the paper.

@article{Jian2022LabelHalluc,
    author = {Yiren Jian and Lorenzo Torresani},
    title = {Label Hallucination for Few-shot Classification},
    journal = {AAAI},
    year = {2022}
}
@article{jian2021label,
      title={Label Hallucination for Few-Shot Classification},
      author={Yiren Jian and Lorenzo Torresani},
      journal={arXiv preprint arXiv:2112.03340},
      year={2021}
}

Requirements

This repo was tested with Ubuntu 18.04.5 LTS, Python 3.6, PyTorch 1.4.0, and CUDA 10.1. You will need at least 32GB RAM and 22GB VRAM (i.e. two Nvidia RTX-2080Ti) for running full experiments in this repo.

Download Data

The data we used here is preprocessed by the repo of MetaOptNet, Please find the renamed versions of the files in below link by RFS.

Download and unzip the dataset, put them under data directory.

Embedding Learning

Please follow RFS, SKD and Rizve et al. (or other transfer learning methods) for the embedding learning. RFS provides a Dropbox link for downloading their pre-trained models for miniImageNet.

We provide our pretrained embedding models by [SKD] and [Rizve et al.] at Dropbox. Note that those models are NOT the official release by original authors, and they perform slightly worse than what reported in their papers. Better models could be trained with longer durations and/or by hyper-parameters tuning.

Once finish the embedding training, put the pre-trained models in models_pretrained directory.

Running Our Fine-tuning

To perform 5-way 5-shot classifications, run:

# For CIFAR-FS
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset CIFAR-FS --data_root data/CIFAR-FS/ --model_path models_pretrained/cifar-fs_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For FC100
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset FC100 --data_root data/FC100/ --model_path models_pretrained/fc100_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For miniImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset miniImageNet --data_root data/miniImageNet/ --model_path models_pretrained/mini_skd_gen1.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat

# For tieredImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel_tieredImageNet.py --dataset tieredImageNet --data_root data/tieredImageNet/ --model_path models_pretrained/tiered_skd_gen0.pth --n_shot 5 --n_aug_support 5  --early 200 --print 50 --norm_feat

To perform 5-way 1-shot classifications, run:

# For CIFAR-FS
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset CIFAR-FS --data_root data/CIFAR-FS/ --model_path models_pretrained/cifar-fs_skd_gen1.pth --n_shot 1 --n_aug_support 25 --epoch 3 --norm_feat

# For FC100
CUDA_VISIBLE_DEVICES=0 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset FC100 --data_root data/FC100/ --model_path models_pretrained/fc100_skd_gen1.pth --n_shot 1 --n_aug_support 25 --epoch 5 --norm_feat

# For miniImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset miniImageNet --data_root data/miniImageNet/ --model_path models_pretrained/mini_skd_gen1.pth --n_shot 1 --n_aug_support 25 --early 150 --norm_feat

# For tieredImageNet (require multiple GPUs)
CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel_tieredImageNet.py --dataset tieredImageNet --data_root data/tieredImageNet/ --model_path models_pretrained/tiered_skd_gen0.pth --n_shot 1 --n_aug_support 25  --early 200 --print 50 --norm_feat

Reading the outputs

400it RFS/SKD/baseline acc: 0.7200 for this episode
==> training...
Epoch: [1][100/288]    Time 0.121 (0.115)    Data 0.001 (0.003)    ..
Epoch: [1][200/288]    Time 0.112 (0.114)    Data 0.001 (0.002)    ...
epoch 400, total time 32.77
acc1: 0.6567, std1: 0.0076, acc2: 0.6820, std2: 0.0080,
epochs: 1, acc2: 0.6400, std2: 0.0080
...

The above is an example print-out for FC100 5-shot. acc1: 0.6567, std1: 0.0076 is the accuracy and the deviation of LinearRegression method with fixed embeddings (used in RFS and SKD). acc2: 0.6820, std2: 0.0080 is the result by our method.

Contacts

For any questions, please contact authors.

Acknowlegements

Thanks to RFS, for the preliminary implementations.

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Comments
  • Reproduce miniImageNet results

    Reproduce miniImageNet results

    Hi, Yiren.

    Thanks very much for sharing your code, and I am really interested in reproducing your works. However, I have difficulty in reproducing the results especially for miniImageNet.

    I run the code with below hyperparameters as provided in README.md.

    • 5-way 5-shot CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset miniImageNet --data_root data/miniImageNet/ --model_path models_pretrained/mini_IER_distilled.pth --n_shot 5 --n_aug_support 5 --epoch 1 --norm_feat
    • 5-way 1shot CUDA_VISIBLE_DEVICES=0,1 python -W ignore eval_fewshot_SoftPseudoLabel.py --dataset miniImageNet --data_root data/miniImageNet/ --model_path models_pretrained/mini_IER_distilled.pth --n_shot 1 --n_aug_support 25 --early 150 --norm_feat

    It gives us accuracies of 67.32 and 86.08 for 1 and 5 shot experiments, respectively, which are lower than 68.28 and 86.54 reported in your manuscript. It would be really appreciate, if you can share the hyperparameters while you use the 'mini_IER_distilled.pth' as the pretrained weights.

    Thank you.

    opened by bungkun 2
  • Request pretrained weight for tieredImageNet

    Request pretrained weight for tieredImageNet

    Hi Yiren,

    Thank you for all the code and pretrained weights you provided. They are really helpful to reproduce your works. I found out that pretrained weights for tieredImageNet is not in the dropbox. Could you please provide the pretrained weights for tieredImageNet too?

    Best.

    opened by bungkun 1
  • The Technical Appendix of your paper

    The Technical Appendix of your paper

    Hi, yiren. Thanks very much for sharing your code! And I am really interested in the content of Technical Appendix which you mentioned in your paper, would you like to share it?

    opened by wanghao14 1
Owner
Yiren Jian
PhD student in Computer Vision and NLP
Yiren Jian
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