Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs). We propose a new perspective for understanding DNN generalization for such datasets, by investigating the dimensionality of the deep representation subspace of training samples. We show that from a dimensionality perspective, DNNs exhibit quite distinctive learning styles when trained with clean labels versus when trained with a proportion of noisy labels. Based on this finding, we develop a new dimensionality-driven learning strategy, which monitors the dimensionality of subspaces during training and adapts the loss function accordingly. We empirically demonstrate that our approach is highly tolerant to significant proportions of noisy labels, and can effectively learn low-dimensional local subspaces that capture the data distribution.
Code (3)
Tasks
Image ClassificationLearning with noisy labelsSimilar Papers 제목 키워드 기반
Noisy multi-label semi-supervised dimensionality reduction
Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not accounted for. How to fully utilize noisy …
Dimensionality ReductionSupervised dimensionality reductionLaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
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 t…
Dimensionality ReductionLearning with noisy labelsMIMO Detection under Hardware Impairments: Learning with Noisy Labels
This paper considers a data detection problem in multiple-input multiple-output (MIMO) communication systems with hardware impairments. To address challenges posed by nonlinear and unknown distortion in received signals,…
Learning with noisy labelsCoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance
Deep neural networks (DNNs) have advanced many machine learning tasks, but their performance is often harmed by noisy labels in real-world data. Addressing this, we introduce CoLafier, a novel approach that uses Local In…
Learning with noisy labelsLearning with Noisy Labels: the Exploration of Error Bounds in Classification
Numerous studies have shown that label noise can lead to poor generalization performance, negatively affecting classification accuracy. Therefore, understanding the effectiveness of classifiers trained using deep neural …
Learning with noisy labels