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Found 4 papers, 1 papers with code
AI-driven platform for systematic nomenclature and intelligent knowledge acquisition of natural medicinal materials
Natural Medicinal Materials (NMMs) have a long history of global clinical applications, accompanied by extensive informational records.
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation
Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data.
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial for visual representation.
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation
This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels generated by itself.
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