LONG-TAILED PARTIAL LABEL LEARNING VIA DYNAMIC REBALANCING

Abstract

Real-world data usually couples the label ambiguity and heavy imbalance, challenging the algorithmic robustness of partial label learning (PLL) and long-tailed learning (LT). The straightforward combination of LT and PLL, i.e., LT-PLL, suffers from a fundamental dilemma: LT methods build upon a given class distribution that is unavailable in PLL, and the performance of PLL is severely influenced in long-tailed context. We show that even with the auxiliary of an oracle class prior, the state-of-the-art methods underperform due to an adverse fact that the constant rebalancing in LT is harsh to the label disambiguation in PLL. To overcome this challenge, we thus propose a dynamic rebalancing method, termed as RECORDS, without assuming any prior knowledge about the class distribution. Based on a parametric decomposition of the biased output, our method constructs a dynamic adjustment that is benign to the label disambiguation process and theoretically converges to the oracle class prior. Extensive experiments on three benchmark datasets demonstrate the significant gain of RECORDS compared with a range of baselines. The code is publicly available.

1. INTRODUCTION

Partial label learning (PLL) origins from the real-world scenarios, where the annotation for each sample is an ambiguous set containing the groundtruth and other confusing labels. This is common when we gather annotations of samples from news websites with several tags (Luo & Orabona, 2010) , videos with several characters of interest (Chen et al., 2018) , or labels from multiple annotators (Gong et al., 2018) . The ideal assumption behind PLL is that the collected data is approximately uniformly distributed regarding classes. However, a natural distribution assumption in above realworld applications should be imbalance, especially follows the long-tailed law, which should be considered if we deploy the PLL methods into online systems. This thereby poses a new challenge about the robustness of algorithms to both category imbalance and label ambiguity in PLL studies. Existing efforts, partial label learning and long-tailed learning, independently study the partial aspect of this problem in the past decades. The standard PLL requires the label disambiguation from candidate sets along with the training of an ordinary classifier (Feng et al., 2020) . The mainstream to solve this problem is estimating label-wise confidence to implicitly or explicitly re-weight the classification loss, e.g., PRODEN (Lv et al., 2020 ), LW (Wen et al., 2021 ), CAVL (Fei et al., 2022) and CORR (Wu et al., 2022) , which have achieved the state-of-the-art performance in PLL. When it comes to the long-tailed learning, the core difficulty lies on diminishing the inherent bias induced by the heavy class imbalance (Chawla et al., 2002; Menon et al., 2013) . The simple but fairly effective method is the logit adjustment (Menon et al., 2021; Ren et al., 2020) , which has been demonstrated very powerful in a range of recent studies (Cui et al., 2021; Narasimhan & Menon, 2021) . Nevertheless, considering a more practical long-tailed partial label learning (LT-PLL) problem, several dilemma remains based on the above two paradigms. One straightforward concern is that the skewed long-tailed distribution exacerbates the bias to the head classes in the label disambiguation, † Corresponding to: Jiangchao Yao (Sunarker@sjtu.edu.cn) and Yanfeng Wang (wangyanfeng@sjtu.edu.cn). 1

