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Found 8 papers, 3 papers with code
Hierarchical Training of Deep Neural Networks Using Early Exiting
In this study, a novel hierarchical training method for deep neural networks is proposed that uses early exits in a divided architecture between edge and cloud workers to reduce the communication cost, training runtime and privacy concerns.
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform
Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge.
Optimizing the Consumption of Spiking Neural Networks with Activity Regularization
Reducing energy consumption is a critical point for neural network models running on edge devices.
Privacy-Preserving Image Acquisition Using Trainable Optical Kernel
Also, in contrast with the previous optical privacy-preserving methods that cannot be trained, our method is data-driven and optimized for the specific application at hand.
Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo
The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision.
Efficient Blind-Spot Neural Network Architecture for Image Denoising
Standard denoising techniques, which use deep neural networks at their core, require pairs of clean and noisy images for its training.
Learning Generative Models using Denoising Density Estimators
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.
Ranked #1 on
Density Estimation
on UCI MINIBOONE
Image Restoration using Plug-and-Play CNN MAP Denoisers
Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type.
