Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification (AAAI 2022)
Prerequisite
- PyTorch >= 1.2.0
- Python3
- torchvision
- argparse
- numpy
Dataset
- Imbalanced CIFAR. The original data will be downloaded and converted by imbalancec_cifar.py
- Imbalanced ImageNet
- The paper also reports results on iNaturalist 2018(https://github.com/visipedia/inat_comp).
CIFAR
CIFAR-LT-100,long-tailed imabalance ratio of 200
python RISDA.py --gpu 3 --lr 0.1 --alpha 0.5 --beta 1 --imb_factor 0.005 --dataset cifar100 --num_classes 100 --save_name simple --idx cifar_im200
CIFAR-LT-100,long-tailed imabalance ratio of 100
python RISDA.py --gpu 3 --lr 0.1 --alpha 0.5 --beta 0.75 --imb_factor 0.01 --dataset cifar100 --num_classes 100 --save_name simple --idx cifar_im100
More details will be uploaded soon.
Acknowledgements
Some codes in this project are adapted from MetaSAug and ISDA. We thank them for their excellent projects.
Citation
If you find this code useful for your research, please cite our paper.
