Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning

TL;DR

This paper introduces a novel class-wise embedding approach for instance-dependent partial label learning, effectively leveraging label relationships and prototypes to disambiguate noisy labels, and demonstrates superior performance on benchmark datasets.

Contribution

It proposes the first class-wise embedding method for IDPLL, utilizing label similarity and prototypes to handle instance-dependent noise and label ambiguity.

Findings

Outperforms twelve existing methods on six benchmark datasets.

Effectively disambiguates noisy labels using class-wise embeddings and prototypes.

Demonstrates robustness on fine-grained datasets.

Abstract

In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and are highly related to the sample features, leading to the instance-dependent partial label learning (IDPLL) problem. Instance-dependent noisy label is a double-edged sword. On one side, it may promote model training as the noisy labels can depict the sample to some extent. On the other side, it brings high label ambiguity as the noisy labels are quite undistinguishable from the ground-truth label. To leverage the nuances of IDPLL effectively, for the first time we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels, i.e., the class-wise embeddings in the candidate label set should have high similarity, while the class-wise embeddings between the candidate label set and the non-candidate label set should have high dissimilarity. Moreover, to reduce the high label ambiguity, we introduce the concept of class prototypes containing global feature information to disambiguate the candidate label set. Extensive experimental comparisons with twelve methods on six benchmark data sets, including four fine-grained data sets, demonstrate the effectiveness of the proposed method. The code implementation is publicly available at https://github.com/Yangfc-ML/CEL.