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pro vyhledávání: '"Test time augmentation"'
Autor:
Pokhrel, Sandesh, Bhandari, Sanjay, Vazquez, Eduard, Lambrou, Tryphon, Gyawali, Prashnna, Bhattarai, Binod
Deep learning has significantly advanced the field of gastrointestinal vision, enhancing disease diagnosis capabilities. One major challenge in automating diagnosis within gastrointestinal settings is the detection of abnormal cases in endoscopic ima
Externí odkaz:
http://arxiv.org/abs/2407.14024
Autor:
Kimura, Masanari, Bondell, Howard
Data augmentation is known to contribute significantly to the robustness of machine learning models. In most instances, data augmentation is utilized during the training phase. Test-Time Augmentation (TTA) is a technique that instead leverages these
Externí odkaz:
http://arxiv.org/abs/2409.12587
Autor:
Sherkatghanad, Zeinab, Abdar, Moloud, Bakhtyari, Mohammadreza, Plawiak, Pawel, Makarenkov, Vladimir
Test-time augmentation (TTA) is a well-known technique employed during the testing phase of computer vision tasks. It involves aggregating multiple augmented versions of input data. Combining predictions using a simple average formulation is a common
Externí odkaz:
http://arxiv.org/abs/2406.17640
In this study, we introduce an intelligent Test Time Augmentation (TTA) algorithm designed to enhance the robustness and accuracy of image classification models against viewpoint variations. Unlike traditional TTA methods that indiscriminately apply
Externí odkaz:
http://arxiv.org/abs/2406.08593
We propose Test-Time Augmentation (TTA) as an effective technique for addressing combinatorial optimization problems, including the Traveling Salesperson Problem. In general, deep learning models possessing the property of invariance, where the outpu
Externí odkaz:
http://arxiv.org/abs/2405.04767
Autor:
Highton, Jack, Chong, Quok Zong, Finestone, Samuel, Beqiri, Arian, Schnabel, Julia A., Bhatia, Kanwal K.
Deep learning models for medical image segmentation and object detection are becoming increasingly available as clinical products. However, as details are rarely provided about the training data, models may unexpectedly fail when cases differ from th
Externí odkaz:
http://arxiv.org/abs/2406.19557
Autor:
Xiong, Haoyu1 (AUTHOR), Yang, Leixin1 (AUTHOR), Fang, Gang1 (AUTHOR), Li, Junwei1 (AUTHOR), Xiang, Yu1 (AUTHOR) xiangyu@ynnu.edu.cn, Zhang, Yaping1 (AUTHOR)
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 2024, Vol. 46 Issue 4, p8783-8798. 16p.
Akademický článek
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Autor:
Kimura, Masanari
Test-Time Augmentation (TTA) is a very powerful heuristic that takes advantage of data augmentation during testing to produce averaged output. Despite the experimental effectiveness of TTA, there is insufficient discussion of its theoretical aspects.
Externí odkaz:
http://arxiv.org/abs/2402.06892
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-20 (2024)
Abstract Stakeholders of machine learning models desire explainable artificial intelligence (XAI) to produce human-understandable and consistent interpretations. In computational toxicity, augmentation of text-based molecular representations has been
Externí odkaz:
https://doaj.org/article/43fa43e4986f442aac4b60eb86525143