Awesome Long-Tailed Learning

A curated list of awesome deep long-tailed learning resources. We recently released Deep Long-Tailed Learning: A Survey to the community. In this survey, we reviewed recent advances in long-tailed learning based on deep neural networks.

Specifically, existing long-tailed learning studies can be grouped into three main categories (i.e., class re-balancing, information augmentation and module improvement), which can be further classified into nine sub-categories (as shown in the below figure). We also empirically analyzed several state-of-the-art methods by evaluating to what extent they address the issue of class imbalance. We concluded the survey by highlighting important applications of deep long-tailed learning and identifying several promising directions for future research. After completing this survey, we decided to release the collected long-tailed learning resources, hoping to push the development of the community. If you have any questions or suggestions, please feel free to contact us.

1. Type of Long-tailed Learning

2. Top-tier Conference Papers

2021

2020

2019

2018

2017

2016

3. Benchmark Datasets

4. Empirical Studies

(1) Long-tailed benchmarking performance

• We evaluate several state-of-the-art methods on ImageNet-LT to see to what extent they handle class imbalance via new evaluation metrics, i.e., UA (upper bound accuracy) and RA (relative accuracy). We categorize these methods based on class re-balancing (CR), information augmentation (IA) and module improvement (MI).

• Almost all long-tailed methods perform better than the Softmax baseline in terms of accuracy, which demonstrates the effectiveness of long-tailed learning.
• Training with 200 epochs leads to better performance for most long-tailed methods, since sufficient training enables deep models to fit data better and learn better image representations.
• In addition to accuracy, we also evaluate long-tailed methods based on UA and RA. For the methods that have higher UA, the performance gain comes not only from the alleviation of class imbalance, but also from other factors, like data augmentation or better network architectures. Therefore, simply using accuracy for evaluation is not accurate enough, while our proposed RA metric provides a good complement, since it alleviates the influences of factors apart from class imbalance.
• For example, MiSLAS, based on data mixup, has higher accuracy than Balanced Sofmtax under 90 training epochs, but it also has higher UA. As a result, the relative accuracy of MiSLAS is lower than Balanced Sofmtax, which means that Balanced Sofmtax alleviates class imbalance better than MiSLAS under 90 training epochs.
• Although some recent high-accuracy methods have lower RA, the overall development trend of long-tailed learning is still positive, as shown in the below figure.

• The current state-of-the-art long-tailed method in terms of both accuracy and RA is TADE (ensemble-based method).

(2) More discussions on cost-sensitive losses

• We further evaluate the performance of different cost-sensitive learning losses based on the decoupled training scheme.
• Decoupled training, compared to joint training, can further improve the overall performance of most cost-sensitive learning methods apart from balanced softmax (BS).
• Although BS outperofmrs other cost-sensitive losses under one-stage training, they perform comparably under decoupled training. This implies that although these cost-sensitive losses perform differently under joint training, they essentially learn similar quality of feature representations.

5. Citation

If this repository is helpful to you, please cite our survey.

@article{zhang2021deep,
  title={Deep long-tailed learning: A survey},
  author={Zhang, Yifan and Kang, Bingyi and Hooi, Bryan and Yan, Shuicheng and Feng, Jiashi},
  journal={arXiv preprint arXiv:2110.04596},
  year={2021}
}

5. Other Resources

• Papers With Code: Long-tailed Learning
• zzw-zwzhang/Awesome-of-Long-Tailed-Recognition
• TADE/Test-Agnostic Long-Tailed Recognition