Results 21 to 30 of about 6,629,786 (241)

Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition

Frontiers in Physiology, 2022
The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis.
Jianguo Zhou, Shangxuan Li, Xuesong Wang, Zizhu Yang, Xinyuan Hou, Wei Lai, Shifeng Zhao, Qingqiong Deng, Wu Zhou   +8 more
doaj   +1 more source

Instance-Level Contrastive Learning for Weakly Supervised Object Detection

Sensors, 2022
Weakly supervised object detection (WSOD) has received increasing attention in object detection field, because it only requires image-level annotations to indicate the presence or absence of target objects, which greatly reduces the labeling costs ...
Ming Zhang, Bing Zeng
doaj   +1 more source

Several Studies of Weakly Supervised Learning in Text Classification [PDF]

, 2022
Text classification is one of the most fundamental tasks in Natural Language Processing. How to effectually utilize the unlabeled dataset in text classification and apply weakly supervised learning methods to further improve the performance based on the ...
Luo, Tianyi
core   +1 more source

Weakly supervised classification in high energy physics

Journal of High Energy Physics, 2017
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations.
Lucio Mwinmaarong Dery, Benjamin Nachman, Francesco Rubbo, Ariel Schwartzman   +3 more
doaj   +1 more source

SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion

Remote Sensing, 2020
The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information ...
Jie Chen, Fen He, Yi Zhang, Geng Sun, Min Deng   +4 more
doaj   +1 more source

Addressing Imbalance in Weakly Supervised Multi-Label Learning

IEEE Access, 2019
Multi-label learning has been widely used in many fields to solve the problem of assigning multiple related categories to an instance. Nevertheless, the label for each training example is assumed complete in most of the current multi-label learning ...
Fang-Fang Luo, Wen-zhong Guo, Guo-Long Chen   +2 more
doaj   +1 more source

Learning Uncertainty-Aware Label Transition for Weakly Supervised Solar Panel Mapping with Aerial Images

, 2022
Weakly supervised solar panel mapping has shown its advantages in automatically detecting solar panels from remote sensing images with low annotation costs.
Zhou, Jun, Zhang, Jue, Hu, Jiankun, Jia, Xiuping   +3 more
core   +1 more source

Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches

Sensors
Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements.
Taoran Sheng, Manfred Huber
doaj   +1 more source

Hybrid weakly supervised learning with deep learning technique for detection of fake news from cyber propaganda

Array, 2023
Due to the emergence of social networking sites and social media platforms, there is faster information dissemination to the public. Unverified information is widely disseminated across social media platforms without any apprehension about the accuracy ...
Liyakathunisa Syed, Abdullah Alsaeedi, Lina A. Alhuri, Hutaf R. Aljohani   +3 more
doaj   +1 more source

Medical image segmentation using deep learning: A survey

IET Image Processing, 2022
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field.
Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi   +5 more
doaj   +1 more source