Jiangchao Yao

Masking: A New Perspective of Noisy Supervision

2018 · arXiv (Cornell University)

It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. …

Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning

2023 · arXiv (Cornell University)

Class incremental learning (CIL) aims to incrementally update a trained model with the new classes of samples (plasticity) while retaining previously learned ability (stability). To address the most challenging issue in this goal, i.e., catastrophic …

Federated Domain Generalization with Generalization Adjustment

2023

Federated Domain Generalization (FedDG) attempts to learn a global model in a privacy-preserving manner that generalizes well to new clients possibly with domain shift. Recent exploration mainly focuses on designing an unbiased training strategy within …

Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning

2024 · arXiv (Cornell University)

Noisy correspondence that refers to mismatches in cross-modal data pairs, is prevalent on human-annotated or web-crawled datasets. Prior approaches to leverage such data mainly consider the application of uni-modal noisy label learning without amending the …