1 citations · 2 across the 11 of their papers we have counts for
5 papers · 1 filter
Learning to Clean: Reinforcement Learning for Noisy Label Correction
Marzi Heidari, Hanping Zhang, Yuhong Guo
The challenge of learning with noisy labels is significant in machine learning, as it can severely degrade the performance of prediction models if not addressed properly. This pape…
Target-Oriented Single Domain Generalization
Marzi Heidari, Yuhong Guo
Deep models trained on a single source domain often fail catastrophically under distribution shifts, a critical challenge in Single Domain Generalization (SDG). While existing meth…
Single Domain Generalization with Adversarial Memory
Hao Yan, Marzi Heidari, Yuhong Guo
Domain Generalization (DG) aims to train models that can generalize to unseen testing domains by leveraging data from multiple training domains. However, traditional DG methods rel…
A Unified Framework for Heterogeneous Semi-supervised Learning
Marzi Heidari, Abdullah Alchihabi, Hao Yan +1
In this work, we introduce a novel problem setup termed as Heterogeneous Semi-Supervised Learning (HSSL), which presents unique challenges by bridging the semi-supervised learning…
Single Domain Generalization with Model-aware Parametric Batch-wise Mixup
Marzi Heidari, Yuhong Guo
Single Domain Generalization (SDG) remains a formidable challenge in the field of machine learning, particularly when models are deployed in environments that differ significantly…