[NeurIPS2024 Spotlight] Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network
Reproduce some methods in semi-supervised papers.
RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT
Mean Teacher-based Cross-Domain Activity Recognition using WiFi Signals, IoTJ 2023
Implementation of semi-supervised learning: UDA, MixMatch, Mean-teacher, focusing on NLP, powered by Pytorch
PyTorch-driven model for efficient vascular segmentation and classification using limited data. Combines semi-supervised and supervised techniques, setting a new standard in resource-efficient auto-segmentation.
Semi supervised learning for semantic image segmentation
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