67 citations · 134 across the 5 of their papers we have counts for
10 papers
Optimizing the Consumption of Spiking Neural Networks with Activity Regularization
Simon Narduzzi, Siavash A. Bigdeli, Shih-Chii Liu +1
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 De…
AIM 2020 Challenge on Image Extreme Inpainting
Evangelos Ntavelis, Andrés Romero, Siavash Bigdeli +1
This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: c…
Efficient Blind-Spot Neural Network Architecture for Image Denoising
David Honzátko, Siavash A. Bigdeli, Engin Türetken +1
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 ima…
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Tiziano Portenier, Siavash Bigdeli, Orcun Goksel
We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural text…
Learning Generative Models using Denoising Density Estimators
Siavash A. Bigdeli, Geng Lin, Tiziano Portenier +2
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…
Image Restoration using Plug-and-Play CNN MAP Denoisers
Siavash Bigdeli, David Honzátko, Sabine Süsstrunk +1
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 (M…