Differences Between Hard and Noisy-labeled Samples: An Empirical Study

TL;DR

This paper empirically investigates the differences between hard-to-learn and noisy-labeled samples, proposing a metric to filter noisy labels, which improves model performance on synthetic and real-world noisy datasets.

Contribution

It introduces a systematic empirical study distinguishing hard and noisy samples and proposes a metric for filtering noisy labels to enhance learning accuracy.

Findings

Filtering noisy labels improves test accuracy.

The proposed metric effectively separates noisy from hard samples.

Method outperforms existing approaches in semi-supervised learning.

Abstract

Extracting noisy or incorrectly labeled samples from a labeled dataset with hard/difficult samples is an important yet under-explored topic. Two general and often independent lines of work exist, one focuses on addressing noisy labels, and another deals with hard samples. However, when both types of data are present, most existing methods treat them equally, which results in a decline in the overall performance of the model. In this paper, we first design various synthetic datasets with custom hardness and noisiness levels for different samples. Our proposed systematic empirical study enables us to better understand the similarities and more importantly the differences between hard-to-learn samples and incorrectly-labeled samples. These controlled experiments pave the way for the development of methods that distinguish between hard and noisy samples. Through our study, we introduce a simple yet effective metric that filters out noisy-labeled samples while keeping the hard samples. We study various data partitioning methods in the presence of label noise and observe that filtering out noisy samples from hard samples with this proposed metric results in the best datasets as evidenced by the high test accuracy achieved after models are trained on the filtered datasets. We demonstrate this for both our created synthetic datasets and for datasets with real-world label noise. Furthermore, our proposed data partitioning method significantly outperforms other methods when employed within a semi-supervised learning framework.