Learn-to-Imagine

Official PyTorch implementation of CVPR2022 paper “Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data” [paper] [Project Page]

Environments

• Python: 3.6.9
• PyTorch: 1.2.0

Training

Training for CIFAR100

Data preparation

CIFAR100 dataset will be downloaded automatically to data_path specified in CIFAR100/options/data/cifar100_3orders.yaml.

For our proposed method, an extra unlabeled dataset is needed for feature generation. To cooperate with CIFAR100, we choose the 32x32 down-sampled ImageNet as the auxiliary unlabeled dataset. Please download the dataset from image-net.org and put it on the data_path/imagenet_32.

Training script

cd CIFAR100;
python -minclearn \
    --options options/Imagine/B50/CIFAR100_B50.yaml options/data/cifar100_3orders.yaml \
    --initial-increment 50 --increment 5 --device YOUR_DEVICES_INDEX --label cifar_b50_step10 -w 0 --save task

Training for ImageNet-Subset

Data preparation

ImageNet100/1000 dataset cannot be downloaded automatically, please download it from image-net.org. Place the dataset in data_path specified in ImageNet/options/data/imagenet100_1order.yaml.

In order to conduct incremental training, we also need to put imagenet split file train_100.txt, val_100.txt into the data path. Symbolic link is recommended:

ln -s ImageNet/imagenet_split/train_100.txt data_path/imagenet1k/train_100.txt
ln -s ImageNet/imagenet_split/val_100.txt data_path/imagenet1k/val_100.txt

For ImageNet100 dataset, we use the rest ImagNet900 data from ImagNet1k as the auxiliary unlabeled dataset. Likely, the corresponding split file should be linked to the data_path.

ln -s ImageNet/imagenet_split/900_100.txt data_path/imagenet1k/train_900.txt

In conclusion, the dataset should be organized like this

data_path
│  
│──imagenet1k
│   │
│   └───train
│       │   n01440764
│       │   n01443537 
│       │   ...
│   │
│   └───val
│       │   n01440764
│       │   n01443537
│       │   ...
│   │   
│   │ train_100.txt
│   │ train_900.txt
│   │ val_100.txt 
│   
└

Training script

 cd ImageNet;
 python -minclearn \
 --options options/Imagine/B50/ImageNet100_B50.yaml options/data/imagenet100_1order.yaml \
 --initial-increment 50 --increment 5 --label Imagine_ImageNet100 -w 4 --device YOUR_DEVICE_INDEX --save task

Acknowledgement

• This repository is heavily based on incremental_learning.pytorch by arthurdouillard.

• If you use this paper/code in your research, please consider citing us:

@inproceedings{tang2022learning,
  title={Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data},
  author={Tang, Yu-Ming and Peng, Yi-Xing and Zheng, Wei-Shi},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}