# Robust Asymmetric Loss for Multi-Label Long-Tailed Learning

Source: [Robust Asymmetric Loss for Multi-Label Long-Tailed Learning](<https://www.sota2.com/research/paper/68fa704a1e209131cea4a3d6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:38:08.126Z

Authors: Wongi Park, Inhyuk Park, Sungeun Kim, Jongbin Ryu

Published: 2023-08-10T12:41:08.000Z

## Abstract

In real medical data, training samples typically show long-tailed distributions with multiple labels. Class distribution of the medical data has a long-tailed shape, in which the incidence of different diseases is quite varied, and at the same time, it is not unusual for images taken from symptomatic patients to be multi-label diseases. Therefore, in this paper, we concurrently address these two issues by putting forth a robust asymmetric loss on the polynomial function. Since our loss tackles both long-tailed and multi-label classification problems simultaneously, it leads to a complex design of the loss function with a large number of hyper-parameters. Although a model can be highly fine-tuned due to a large number of hyper-parameters, it is difficult to optimize all hyper-parameters at the same time, and there might be a risk of overfitting a model. Therefore, we regularize the loss function using the Hill loss approach, which is beneficial to be less sensitive against the numerous hyper-parameters so that it reduces the risk of overfitting the model. For this reason, the proposed loss is a generic method that can be applied to most medical image classification tasks and does not make the training process more time-consuming. We demonstrate that the proposed robust asymmetric loss performs favorably against the long-tailed with multi-label medical image classification in addition to the various long-tailed single-label datasets. Notably, our method achieves Top-5 results on the CXR-LT dataset of the ICCV CVAMD 2023 competition. We opensource our implementation of the robust asymmetric loss in the public repository: https://github.com/kalelpark/RAL.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2308.05542>)

[PDF](<https://arxiv.org/pdf/2308.05542.pdf>)