Results 41 to 50 of about 44,296 (265)

Towards Realistic Semi-supervised Learning

, 2022
Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation ...
Mamshad Nayeem Rizve, Navid Kardan, Mubarak Shah   +2 more
openaire   +2 more sources

Semi-supervised Vocabulary-Informed Learning [PDF]

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with ...
Yanwei Fu 0001, Leonid Sigal
openaire   +2 more sources

A Semi-Supervised-Learning-Aided Explainable Belief Rule-Based Approach to Predict the Energy Consumption of Buildings

Algorithms
Predicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the
Sami Kabir, Mohammad Shahadat Hossain, Karl Andersson   +2 more
doaj   +1 more source

Full body virtual try‐on with semi‐self‐supervised learning

Electronics Letters, 2021
This paper proposes a full body virtual try‐on which handles both top and bottom garments and generates realistic try‐on images. For the full body virtual try‐on, this paper addresses lack of suitable training data to align and fit top and bottom ...
Hyug‐Jae Lee, Byumhyuk Koo, Ha‐Eun Ahn, Minseok Kang, Rokkyu Lee, Gunhan Park   +5 more
doaj   +1 more source

An Efficient Approach to Select Instances in Self-Training and Co-Training Semi-Supervised Methods

IEEE Access, 2022
Semi-supervised learning is a machine learning approach that integrates supervised and unsupervised learning mechanisms. In this learning, most of labels in the training set are unknown, while there is a small part of data that has known labels. The semi-
Karliane Medeiros Ovidio Vale, Arthur Costa Gorgonio, Flavius Da Luz E Gorgonio, Anne Magaly De Paula Canuto   +3 more
doaj   +1 more source

Semi-supervised few-shot learning approach for plant diseases recognition

Plant Methods, 2021
Background Learning from a few samples to automatically recognize the plant leaf diseases is an attractive and promising study to protect the agricultural yield and quality.
Yang Li, Xuewei Chao
doaj   +1 more source

A Survey On Semi-Supervised Learning Techniques [PDF]

International Journal of Computer Trends and Technology, 2014
5 Pages, 3 figures, Published with International Journal of Computer Trends and Technology (IJCTT)
V. Jothi Prakash, L. M. Nithya
openaire   +2 more sources

Pathways and pitfalls: a qualitative study of student experiences in biomedical science education

FEBS Open Bio, EarlyView.
Biomedical science students from underrepresented backgrounds face barriers including financial strain, disrupted laboratory access and cultural exclusion. Peer networks provide vital support when institutional systems are difficult to navigate. To create inclusive learning environments and achieve academic success, educators should blend active, hands‐
Olivia J. Russell, Cassy Ross, Panagiota S. Filippou, Bruno Silvester Lopes, Dominic Gilchrist, Paul Dean, Kamar Ameen‐Ali, Tora K. Smulders‐Srinivasan, Ahmad A. Khundakar   +8 more
wiley   +1 more source

Cardiac Imaging with Electrical Impedance Tomography (EIT) using Multilayer Perceptron Network

Jurnal Elektronika dan Telekomunikasi
This research explores the enhancement of Electrical Impedance Tomography (EIT) for cardiac imaging using Multilayer Perceptron (MLP) networks, focusing on supervised and semi-supervised learning approaches.
Amelia Putri Ristyawardani, Marlin Ramadhan Baidillah, Yudi Adityawarman, Pratondo Busono, Mochamad Adityo Rachmadi, Meta Yantidewi, Endah Rahmawati   +6 more
doaj   +1 more source

Semi-supervised learning in cancer diagnostics

Frontiers in Oncology, 2022
In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making.
Jan-Niklas Eckardt, Jan-Niklas Eckardt, Martin Bornhäuser, Martin Bornhäuser, Martin Bornhäuser, Karsten Wendt, Karsten Wendt, Jan Moritz Middeke, Jan Moritz Middeke   +8 more
doaj   +1 more source