Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Related tags

Deep Learning RISDA
Overview

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.

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Comments
  • Accurate Rate or Error Rate?

    Accurate Rate or Error Rate?

    I download code in your repository and read it carefully and thoroughly.

    I notice that the code keeps the manual random seed the same and sets cudnn.deterministic True. All of this makes sure the result is asthe same as you report in the paper. Specifically, when Imbalance Factor is 20, the result is 41.33.

    However, according to the code, 41.33 is calculated as accuracy rate, while in your paper, 41.33 is reported as error rate. The 2 metrics are opposite aspect of an algo, which makes me puzzled.

    Could you give an explanation?

    opened by DemonsHunter 1
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