scholarly journals Weakly supervised classification model for zero-shot semantic segmentation

2020 ◽  
Vol 56 (23) ◽  
pp. 1247-1250
Author(s):  
Fengli Shen ◽  
Zong-Hui Wang ◽  
Zhe-Ming Lu
Keyword(s):  
Supervised Classification ◽  
Semantic Segmentation ◽  
Classification Model ◽  
Weakly Supervised ◽  
Weakly Supervised Classification

Multiple kernel-based dictionary learning for weakly supervised classification

2015 ◽  
Vol 48 (8) ◽  
pp. 2667-2675 ◽  
Author(s):  
Ashish Shrivastava ◽  
Jaishanker K. Pillai ◽  
Vishal M. Patel
Keyword(s):  
Dictionary Learning ◽  
Supervised Classification ◽  
Multiple Kernel ◽  
Weakly Supervised ◽  
Weakly Supervised Classification

scholarly journals Weakly Supervised Classification For High Energy Physics

2018 ◽  
Vol 1085 ◽  
pp. 042006 ◽  
Author(s):  
Lucio Mwinmaarong Dery ◽  
Benjamin Nachman ◽  
Francesco Rubbo ◽  
Ariel Schwartzman
Keyword(s):  
Supervised Classification ◽  
High Energy Physics ◽  
High Energy ◽  
Weakly Supervised ◽  
Weakly Supervised Classification ◽  
Energy Physics

scholarly journals Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

2021 ◽  
Vol 13 (24) ◽  
pp. 5009
Author(s):  
Lingbo Huang ◽  
Yushi Chen ◽  
Xin He
Keyword(s):  
Supervised Classification ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
The State ◽  
Weak Supervision ◽  
Training Samples ◽  
Weakly Supervised ◽  
Weakly Supervised Classification

In recent years, supervised learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification. However, the collection of training samples with labels is not only costly but also time-consuming. This fact usually causes the existence of weak supervision, including incorrect supervision where mislabeled samples exist and incomplete supervision where unlabeled samples exist. Focusing on the inaccurate supervision and incomplete supervision, the weakly supervised classification of HSI is investigated in this paper. For inaccurate supervision, complementary learning (CL) is firstly introduced for HSI classification. Then, a new method, which is based on selective CL and convolutional neural network (SeCL-CNN), is proposed for classification with noisy labels. For incomplete supervision, a data augmentation-based method, which combines mixup and Pseudo-Label (Mix-PL) is proposed. And then, a classification method, which combines Mix-PL and CL (Mix-PL-CL), is designed aiming at better semi-supervised classification capacity of HSI. The proposed weakly supervised methods are evaluated on three widely-used hyperspectral datasets (i.e., Indian Pines, Houston, and Salinas datasets). The obtained results reveal that the proposed methods provide competitive results compared to the state-of-the-art methods. For inaccurate supervision, the proposed SeCL-CNN has outperformed the state-of-the-art method (i.e., SSDP-CNN) by 0.92%, 1.84%, and 1.75% in terms of OA on the three datasets, when the noise ratio is 30%. And for incomplete supervision, the proposed Mix-PL-CL has outperformed the state-of-the-art method (i.e., AROC-DP) by 1.03%, 0.70%, and 0.82% in terms of OA on the three datasets, with 25 training samples per class.

scholarly journals Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Jason A. Fries ◽  
Paroma Varma ◽  
Vincent S. Chen ◽  
Ke Xiao ◽  
Heliodoro Tejeda ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Cardiac Mri ◽  
Supervised Classification ◽  
Mri Sequences ◽  
Weakly Supervised ◽  
Weakly Supervised Classification

scholarly journals Weakly supervised classification in high energy physics

2017 ◽  
Vol 2017 (5) ◽  
Author(s):  
Lucio Mwinmaarong Dery ◽  
Benjamin Nachman ◽  
Francesco Rubbo ◽  
Ariel Schwartzman
Keyword(s):  
Supervised Classification ◽  
High Energy Physics ◽  
High Energy ◽  
Weakly Supervised ◽  
Weakly Supervised Classification ◽  
Energy Physics
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