LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
Mingcai Chen Yuntao Du Wei Tang Baoming Zhang Hao Cheng Shuwei Qian Chongjun Wang
In real-world applications, perfect labels are rarely available, making it
challenging to develop robust machine learning algorithms that can handle noisy
labels. Recent methods have focused on filtering noise based on the discrepancy
between model predictions and given noisy labels, assuming that samples with
small classification losses are clean. This work takes a different approach by
leveraging the consistency between the learned model and the entire noisy
dataset using the rich representational and topological information in the
data. We introduce LaplaceConfidence, a method that to obtain label confidence
(i.e., clean probabilities) utilizing the Laplacian energy. Specifically, it
first constructs graphs based on the feature representations of all noisy
samples and minimizes the Laplacian energy to produce a low-energy graph. Clean
labels should fit well into the low-energy graph while noisy ones should not,
allowing our method to determine data's clean probabilities. Furthermore,
LaplaceConfidence is embedded into a holistic method for robust training, where
co-training technique generates unbiased label confidence and label
refurbishment technique better utilizes it. We also explore the dimensionality
reduction technique to accommodate our method on large-scale noisy datasets.
Our experiments demonstrate that LaplaceConfidence outperforms state-of-the-art
methods on benchmark datasets under both synthetic and real-world noise.