Zobrazeno 1 - 10
of 341
pro vyhledávání: '"Chen Liang-Gee"'
Pruning has become a promising technique used to compress and accelerate neural networks. Existing methods are mainly evaluated on spare labeling applications. However, dense labeling applications are those closer to real world problems that require
Externí odkaz:
http://arxiv.org/abs/2101.06686
Lossy image compression is often limited by the simplicity of the chosen loss measure. Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures
Externí odkaz:
http://arxiv.org/abs/2012.01874
We investigate pruning and quantization for deep neural networks. Our goal is to achieve extremely high sparsity for quantized networks to enable implementation on low cost and low power accelerator hardware. In a practical scenario, there are partic
Externí odkaz:
http://arxiv.org/abs/2010.01892
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2010, Iss 1, p 675784 (2010)
Abandoned luggage represents a potential threat to public safety. Identifying objects as luggage, identifying the owners of such objects, and identifying whether owners have left luggage behind are the three main problems requiring solution. This pap
Externí odkaz:
https://doaj.org/article/8b5ae2dfc60e44af901bdae5867eeb65
Digital media is ubiquitous and produced in ever-growing quantities. This necessitates a constant evolution of compression techniques, especially for video, in order to maintain efficient storage and transmission. In this work, we aim at exploiting n
Externí odkaz:
http://arxiv.org/abs/1910.08737
Video coding is a critical step in all popular methods of streaming video. Marked progress has been made in video quality, compression, and computational efficiency. Recently, there has been an interest in finding ways to apply techniques form the fa
Externí odkaz:
http://arxiv.org/abs/1905.04326
Scene Parsing is a crucial step to enable autonomous systems to understand and interact with their surroundings. Supervised deep learning methods have made great progress in solving scene parsing problems, however, come at the cost of laborious manua
Externí odkaz:
http://arxiv.org/abs/1903.09781
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