Summary of Layermatch: Do Pseudo-labels Benefit All Layers?, by Chaoqi Liang et al.
LayerMatch: Do Pseudo-labels Benefit All Layers?by Chaoqi Liang, Guanglei Yang, Lifeng Qiao, Zitong Huang, Hongliang…
LayerMatch: Do Pseudo-labels Benefit All Layers?by Chaoqi Liang, Guanglei Yang, Lifeng Qiao, Zitong Huang, Hongliang…
You can’t handle the (dirty) truth: Data-centric insights improve pseudo-labelingby Nabeel Seedat, Nicolas Huynh, Fergus…
Boosting Consistency in Dual Training for Long-Tailed Semi-Supervised Learningby Kai Gan, Tong Wei, Min-Ling ZhangFirst…
Towards Bayesian Data Selectionby Julian RodemannFirst submitted to arxiv on: 18 Jun 2024CategoriesMain: Machine Learning…
Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selectionby Yanyong Huang, Li Yang, Dongjie Wang, Ke…
Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detectionby Mehar Khurana, Neehar Peri, James Hays, Deva RamananFirst…
POWN: Prototypical Open-World Node Classificationby Marcel Hoffmann, Lukas Galke, Ansgar ScherpFirst submitted to arxiv on:…
Generative vs. Discriminative modeling under the lens of uncertainty quantificationby Elouan Argouarc'h, François Desbouvries, Eric Barat,…
GKAN: Graph Kolmogorov-Arnold Networksby Mehrdad Kiamari, Mohammad Kiamari, Bhaskar KrishnamachariFirst submitted to arxiv on: 10…
A Dual-View Approach to Classifying Radiology Reports by Co-Trainingby Yutong Han, Yan Yuan, Lili MouFirst…