Results 51 to 60 of about 2,516,559 (186)

Efficient Deep Feature Learning and Extraction via StochasticNets

, 2015
Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data.
Fieguth, Paul, Shafiee, Mohammad Javad, Siva, Parthipan, Wong, Alexander   +3 more
core   +1 more source

Learning visual features under motion invariance [PDF]

Neural Networks, 2020
73 pages, 9 figures.
Betti A., Gori M., Melacci S.
openaire   +5 more sources

A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation

Sensors, 2020
In a real-world application, the images taken by different cameras with different conditions often incur illumination variation, low-resolution, different poses, blur, etc., which leads to a large distribution difference or gap between training (source ...
Rakesh Kumar Sanodiya, Leehter Yao
doaj   +1 more source

Vibration Image Representations for Fault Diagnosis of Rotating Machines: A Review

Machines, 2022
Rotating machine vibration signals typically represent a large collection of responses from various sources in a machine, along with some background noise.
Hosameldin Osman Abdallah Ahmed, Asoke Kumar Nandi   +1 more
doaj   +1 more source

Discriminative learning of apparel features [PDF]

2015 14th IAPR International Conference on Machine Vision Applications (MVA), 2015
ISBN:978-4-901122-14 ...
Rothe, Rasmus, Ristin, Marko, Dantone, Matthias, Van Gool, Luc   +3 more
openaire   +2 more sources

Convex multi-task feature learning [PDF]

Machine Learning, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Argyriou, Andreas, Evgeniou, Theodoros, Pontil, Massimiliano   +2 more
openaire   +2 more sources

Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting

IEEE Access, 2019
Illumination-insensitive image representation is a great challenge in the computer vision field. Illumination variations considerably obstruct the effectiveness of image feature extraction.
Tao Gao, Ting Chen, C. C. Wang, Zhanwen Liu, Wei Lu, Y. H. Li   +5 more
doaj   +1 more source

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel.
Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Jocelyn Chanussot   +9 more
doaj   +1 more source

Supervised learning with quantum enhanced feature spaces

, 2018
Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems.
Chow, Jerry M., Córcoles, Antonio D., Gambetta, Jay M., Harrow, Aram W., Havlicek, Vojtech, Kandala, Abhinav, Temme, Kristan   +6 more
core   +1 more source

Adversarial Feature Learning

, 2016
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution.
Donahue, Jeff, Krähenbühl, Philipp, Darrell, Trevor   +2 more
openaire   +2 more sources