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Concept

Deep learning super sampling

Publications associées (7)

Multi-memristive synaptic architectures for training neural networks

Irem Boybat Kara

Highly data-centric AI workloads require that new computing paradigms be adopted because the performance of traditional CPU- and GPU-based systems are limited by data access and transfer. Training deep neural networks with millions of tunable parameters ta ...

EPFL2020

Kernel Modeling Super-Resolution on Real Low-Resolution Images

Sabine Süsstrunk, Ruofan Zhou

Deep convolutional neural networks (CNNs), trained on corresponding pairs of high- and low-resolution images, achieve state-of-the-art performance in single-image super- resolution and surpass previous signal-processing based approaches. However, their per ...

IEEE COMPUTER SOC2019

Real-time DCT Learning-based Reconstruction of Neural Signals

Volkan Cevher, Cosimo Aprile, Rabeeh Karimi Mahabadi

Wearable and implantable body sensor network systems are one of the key technologies for continuous monitoring of patient’s vital health status such as temperature and blood pressure, and brain activity. Such devices are critical for early detection of eme ...

IEEE2018

3D-Stacked CMOS SPAD Image Sensors: Technology and Applications

Edoardo Charbon, Claudio Bruschini, Myung Jae Lee

3Dstacked CMOS SPAD based image sensors hold the promise for better sensitivity and more functionality per pixel. The technology enables to separate detection from computation onto different chips, or tiers, that are stacked onto one another. One advantage ...

IEEE2018

Discriminative Learning of Deep Convolutional Feature Point Descriptors

Pascal Fua, Eduard Trulls Fortuny

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patc ...

2015

Parallel Rendering on Hybrid Multi-GPU Clusters

Achieving efﬁcient scalable parallel rendering for interactive visualization applications on medium-sized graphics clusters remains a challenging problem. Framerates of up to 60hz require a carefully designed and ﬁne-tuned parallel rendering implementation ...

The Eurographics Association2012

Spatial Super-Resolution of a Diffusion Field by Temporal Oversampling in Sensor Networks

Martin Vetterli, Yue Lu

We study the spatial-temporal sampling of a linear diffusion field, and show that it is possible to compensate for insufficient spatial sampling densities by oversampling in time. Our work is motivated by the following issue often encountered in sensor net ...

2009