  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.

Machine Learning;Computer Vision and Pattern Recognition