Results 1 to 10 of about 2,965,444 (278)
RenderGAN: Generating Realistic Labeled Data [PDF]
Frontiers in Robotics and AI, 2018 Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting.
Leon Sixt, Benjamin Wild, Tim Landgraf
doaj +6 more sources
MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data [PDF]
Diagnostics, 2023 Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses when it comes to assessing certain edge cases.
Dennis Hartmann +6 more
doaj +2 more sources
Editorial: Deep learning with limited labeled data for vision, audio, and text [PDF]
Frontiers in Artificial Intelligence, 2023 Marko Orescanin +4 more
doaj +2 more sources
Active clustering data streams with affinity propagation
ICT Express, 2022 Most existing applications have a large number of evolving data streams. Clustering data streams is still a critical problem for these applications as the data are evolving and changes over time.
Sameh Abdulah, Ph.D. +2 more
doaj +1 more source
Incremental Feature Selection Algorithm for Dynamic Partially Labeled Hybrid Data [PDF]
Jisuanji kexue, 2022 Many real-world data sets are hybrid data consisting of symbolic,numerical and missing features.For the decision labels of hybrid data,it costs much labor and it is expensive to acquire the decision labels of all data,thus the partially labeled data is ...
YAN Zhen-chao, SHU Wen-hao, XIE Xin
doaj +1 more source
BMC Bioinformatics, 2023 Background The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its
Shaima Belhechmi +4 more
doaj +1 more source
Subpath Queries on Compressed Graphs: A Survey
Algorithms, 2021 Text indexing is a classical algorithmic problem that has been studied for over four decades: given a text T, pre-process it off-line so that, later, we can quickly count and locate the occurrences of any string (the query pattern) in T in time ...
Nicola Prezza
doaj +1 more source
FGCM: Noisy Label Learning via Fine-Grained Confidence Modeling
Applied Sciences, 2022 A small portion of mislabeled data can easily limit the performance of deep neural networks (DNNs) due to their high capacity for memorizing random labels.
Shaotian Yan +3 more
doaj +1 more source
You Don’t Need Labeled Data for Open-Book Question Answering
Applied Sciences, 2021 Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic.
Sia Gholami, Mehdi Noori
doaj +1 more source
Seismic labeled data expansion using variational autoencoders
Artificial Intelligence in Geosciences, 2020 Supervised machine learning algorithms have been widely used in seismic exploration processing, but the lack of labeled examples complicates its application.
Kunhong Li, Song Chen, Guangmin Hu, Ph.D
doaj +1 more source