no code implementations • 2 May 2024 • Marzi Heidari, Hanping Zhang, Yuhong Guo
In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce.
no code implementations • 17 Apr 2024 • Marzi Heidari, Hanping Zhang, Yuhong Guo
In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL).
no code implementations • 10 Sep 2022 • Hanping Zhang, Yuhong Guo
As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL).
no code implementations • 29 Jun 2021 • Hanping Zhang, Yuhong Guo
In this work, we propose a novel policy-aware adversarial data augmentation method to augment the standard policy learning method with automatically generated trajectory data.