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pro vyhledávání: '"Bespalov, Iaroslav"'
Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets, e.g., when the sets are produced by different hardware. As a consequence of this
Externí odkaz:
http://arxiv.org/abs/2208.00474
Publikováno v:
IEEE Access 2022
The training of Generative Adversarial Networks (GANs) requires a large amount of data, stimulating the development of new augmentation methods to alleviate the challenge. Oftentimes, these methods either fail to produce enough new data or expand the
Externí odkaz:
http://arxiv.org/abs/2104.00925
Publikováno v:
Computer Vision and Image Understanding, V. 223, 103519, 2022
We devise a universal adaptive neural layer to "learn" optimal frequency filter for each image together with the weights of the base neural network that performs some computer vision task. The proposed approach takes the source image in the spatial d
Externí odkaz:
http://arxiv.org/abs/2010.01177
Publikováno v:
Pattern Recognition, V. 131, 108816, 2022
Unsupervised retrieval of image features is vital for many computer vision tasks where the annotation is missing or scarce. In this work, we propose a new unsupervised approach to detect the landmarks in images, validating it on the popular task of h
Externí odkaz:
http://arxiv.org/abs/2006.11643
Publikováno v:
In Pattern Recognition November 2022 131
Publikováno v:
In Computer Vision and Image Understanding October 2022 223
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