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pro vyhledávání: '"L. D'Andrea"'
The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training dataset contains sensitive content, e.g., face images. In this work, we
Externí odkaz:
http://arxiv.org/abs/2408.05092
Deep neural networks provide state-of-the-art accuracy for vision tasks but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication c
Externí odkaz:
http://arxiv.org/abs/2303.02384
Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Network models. However, not much emphasis is put on adapting the network t
Externí odkaz:
http://arxiv.org/abs/2208.11011
Reducing energy consumption is a critical point for neural network models running on edge devices. In this regard, reducing the number of multiply-accumulate (MAC) operations of Deep Neural Networks (DNNs) running on edge hardware accelerators will r
Externí odkaz:
http://arxiv.org/abs/2204.01460
Preserving privacy is a growing concern in our society where sensors and cameras are ubiquitous. In this work, for the first time, we propose a trainable image acquisition method that removes the sensitive identity revealing information in the optica
Externí odkaz:
http://arxiv.org/abs/2106.14577
The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects such as inter-reflections or cast shadows makes the task particularly
Externí odkaz:
http://arxiv.org/abs/2103.12106
Publikováno v:
2020 7th Swiss Conference on Data Science (SDS), Luzern, Switzerland, 2020, pp. 59-60
Image denoising is an essential tool in computational photography. Standard denoising techniques, which use deep neural networks at their core, require pairs of clean and noisy images for its training. If we do not possess the clean samples, we can u
Externí odkaz:
http://arxiv.org/abs/2008.11010
Learning probabilistic models that can estimate the density of a given set of samples, and generate samples from that density, is one of the fundamental challenges in unsupervised machine learning. We introduce a new generative model based on denoisi
Externí odkaz:
http://arxiv.org/abs/2001.02728
Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a
Externí odkaz:
http://arxiv.org/abs/1912.09299
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