A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization

This is a Pytorch implementation of our paper:

• Journal Version (TPAMI 2026): Zitai Wang, Qianqian Xu, Zhiyong Yang, Zhikang Xu, Linchao Zhang, Xiaochun Cao, Qingming Huang. A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI, IF: 18.6), 48(1): 639-656, Jan. 2026.

• Conference Version (NeurIPS 2023 Spotlight): Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang. A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning. Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.

Our codes are based on the repositories Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data. We have improved the reproducibility, and the results might be sightly different from those reported in the paper.

Dependencies

Please refer to the requirements.yml in the root folder.

Scripts

Pleas refer to the bash_script.sh in the root folder, where the scrpts can reproduce the results under the setting 400 Epochs with tuned wd, SAM, and RandAug (see below).

Results (Conference Version)

CIFAR-100

• 200 Epochs with wd=0.0002 (standard setting)

	LT ($\rho=100$)	LT ($\rho=10$)	Step ($\rho=100$)	Step ($\rho=10$)
CE	38.06 ± 1.03	56.73 ± 0.12	38.74 ± 0.13	54.50 ± 0.22
LDAM + DRW	42.73 ± 0.28	57.75 ± 0.54	46.01 ± 0.16	56.84 ± 0.20
VS	42.67 ± 0.48	58.50 ± 0.21	46.74 ± 0.47	59.97 ± 0.06
VS + ADRW + TLA (Ours)	43.20 ± 0.05	58.90 ± 0.30	47.60 ± 0.27	60.12 ± 0.22

• 200 Epochs with tuned wd

	LT ($\rho=100$)	LT ($\rho=10$)	Step ($\rho=100$)	Step ($\rho=10$)
CE	40.68 ± 0.26	58.80 ± 0.24	38.91 ± 0.05	54.56 ± 0.13
LDAM + DRW	45.44 ± 0.06	58.31 ± 0.32	46.41 ± 0.28	57.14 ± 0.23
VS	46.26 ± 0.31	61.11 ± 0.26	46.74 ± 0.47	61.02 ± 0.31
VS + ADRW + TLA (Ours)	46.29 ± 0.50	61.33 ± 0.25	47.76 ± 0.31	61.39 ± 0.35

• 400 Epochs with tuned wd, SAM, and RandAug

	LT ($\rho=100$)	LT ($\rho=10$)	Step ($\rho=100$)	Step ($\rho=10$)
CE	45.70 ± 0.35	63.11 ± 0.19	41.98 ± 0.15	59.26 ± 0.24
LDAM + DRW	50.67 ± 0.25	61.55 ± 0.05	50.62 ± 0.24	60.08 ± 0.14
VS	52.09 ± 0.12	65.23 ± 0.24	49.77 ± 0.42	64.28 ± 0.04
VS + ADRW + TLA (Ours)	53.05 ± 0.12	65.59 ± 0.28	51.69 ± 0.29	64.98 ± 0.13

CIFAR-10

• 200 Epochs with wd=0.0002 (standard setting)

	LT ($\rho=100$)	LT ($\rho=10$)	Step ($\rho=100$)	Step ($\rho=10$)
CE	71.25 ± 0.10	86.74 ± 0.19	64.81 ± 1.22	85.43 ± 0.09
LDAM + DRW	77.97 ± 0.17	87.96 ± 0.16	78.27 ± 0.44	87.65 ± 0.22
VS	80.29 ± 0.23	88.74 ± 0.24	80.19 ± 0.09	89.13 ± 0.00
VS + ADRW + TLA (Ours)	80.81 ± 0.05	88.86 ± 0.13	80.78 ± 0.15	89.24 ± 0.10

• 200 Epochs with tuned wd

	LT ($\rho=100$)	LT ($\rho=10$)	Step ($\rho=100$)	Step ($\rho=10$)
CE	73.39 ± 0.30	87.43 ± 0.22	66.20 ± 0.36	85.57 ± 0.11
LDAM + DRW	79.81 ± 0.35	87.96 ± 0.16	78.71 ± 0.14	87.67 ± 0.23
VS	81.62 ± 0.10	89.10 ± 0.13	81.25 ± 0.23	89.31 ± 0.12
VS + ADRW + TLA (Ours)	81.64 ± 0.07	89.20 ± 0.11	81.29 ± 0.22	89.57 ± 0.09

• 400 Epochs with tuned wd, SAM, and RandAug

	LT ($\rho=100$)	LT ($\rho=10$)	Step ($\rho=100$)	Step ($\rho=10$)
CE	79.75 ± 0.46	90.79 ± 0.04	70.13 ± 0.32	88.63 ± 0.22
LDAM + DRW	86.15 ± 0.16	91.17 ± 0.10	84.48 ± 0.38	91.20 ± 0.04
VS	86.29 ± 0.13	91.75 ± 0.09	85.04 ± 0.16	91.68 ± 0.08
VS + ADRW + TLA (Ours)	86.42 ± 0.10	91.82 ± 0.16	85.55 ± 0.22	91.80 ± 0.04

Results (Journal Version)

Please refer to the journal version of our paper for more comprehensive experiments.

Citation

@article{wang2026unified,
    title = {A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization},
    author = {Zitai Wang and Qianqian Xu and Zhiyong Yang and Zhikang Xu and Linchao Zhang and Xiaochun Cao and Qingming Huang},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    volume = {48},
    number = {1},
    pages = {639--656},
    year = {2026},
}

@InProceedings{wang2023unified,
    title = {A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning},
    author = {Zitai Wang and Qianqian Xu and Zhiyong Yang and Yuan He and Xiaochun Cao and Qingming Huang},
    booktitle = {Annual Conference on Neural Information Processing Systems},
    year = {2023},
    pages = {48417--48430}
}