self-supervised learning

Self-supervised Learning on EEG Data for Emotion Prediction
This project involves using self-supervised learning on EEG data to predict emotions, addressing the challenge of expensive and error-prone labeled EEG data by leveraging the unlabeled TUH dataset for pretraining.
Self-supervised Learning on EEG Data for Emotion Prediction
3D Object Part Segmentation with Self-supervised Learning
Validated the effectiveness of self-supervised learning in achieving state-of-the-art results with limited label availability, revolutionizing the approach to 3D object part segmentation.
3D Object Part Segmentation with Self-supervised Learning
Weakly-supervised Semantic Segmentation through Projective Cycle-consistency
Employed self-supervised segmentation techniques to autonomously learn intricate scene understanding tasks with limited annotations, specifically in medical contexts such as surgical operating rooms.
Weakly-supervised Semantic Segmentation through Projective Cycle-consistency
Exploiting Multi-Modality Context for Enhanced Online Adaptive Pseudo-Labeling of Point Clouds
This thesis addresses the challenge of weakly supervised point cloud semantic segmentation by leveraging multi-modal information and introducing novel pseudo-labeling techniques.
Mert Kıray
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Exploiting Multi-Modality Context for Enhanced Online Adaptive Pseudo-Labeling of Point Clouds