704 citations · 3.6k across the 98 of their papers we have counts for
18 papers · 1 filter
A Greedy Approach to Based Convolutional Sparse Coding
Elad Plaut, Raja Giryes
Sparse coding techniques for image processing traditionally rely on a processing of small overlapping patches separately followed by averaging. This has the disadvantage that the r…
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware
Natan Liss, Chaim Baskin, Avi Mendelson +2
Convolutional Neural Networks (CNN) has become more popular choice for various tasks such as computer vision, speech recognition and natural language processing. Thanks to their la…
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal Learning
Tom Tirer, Raja Giryes
While deep neural networks exhibit state-of-the-art results in the task of image super-resolution (SR) with a fixed known acquisition process (e.g., a bicubic downscaling kernel),…
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Chaim Baskin, Natan Liss, Yoav Chai +5
Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning…
An ETF view of Dropout regularization
Dor Bank, Raja Giryes
Dropout is a popular regularization technique in deep learning. Yet, the reason for its success is still not fully understood. This paper provides a new interpretation of Dropout f…
MeshCNN: A Network with an Edge
Rana Hanocka, Amir Hertz, Noa Fish +3
Polygonal meshes provide an efficient representation for 3D shapes. They explicitly capture both shape surface and topology, and leverage non-uniformity to represent large flat reg…
Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
Tal Remez, Or Litany, Raja Giryes +1
We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising p…
Generalization Error in Deep Learning
Daniel Jakubovitz, Raja Giryes, Miguel R. D. Rodrigues
Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However…
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion
Tal Levy, Alireza Vahid, Raja Giryes
This paper proposes a new method for solving the well-known rank aggregation problem from pairwise comparisons using the method of low-rank matrix completion. The partial and noisy…
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Eli Schwartz, Leonid Karlinsky, Joseph Shtok +6
Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision. In this work, we proposes a simple yet effective met…
RepMet: Representative-based metric learning for classification and one-shot object detection
Leonid Karlinsky, Joseph Shtok, Sivan Harary +5
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each ca…
DNN or k-NN: That is the Generalize vs. Memorize Question
Gilad Cohen, Guillermo Sapiro, Raja Giryes
This paper studies the relationship between the classification performed by deep neural networks (DNNs) and the decision of various classical classifiers, namely k-nearest neighbou…
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks
Chaim Baskin, Eli Schwartz, Evgenii Zheltonozhskii +4
We present a novel method for neural network quantization that emulates a non-uniform -quantile quantizer, which adapts to the distribution of the quantized parameters. Our appr…
ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning
Rana Hanocka, Noa Fish, Zhenhua Wang +3
The process of aligning a pair of shapes is a fundamental operation in computer graphics. Traditional approaches rely heavily on matching corresponding points or features to guide…
The Resistance to Label Noise in K-NN and DNN Depends on its Concentration
Amnon Drory, Oria Ratzon, Shai Avidan +1
We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's p…
Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization
Daniel Jakubovitz, Raja Giryes
Deep neural networks have lately shown tremendous performance in various applications including vision and speech processing tasks. However, alongside their ability to perform thes…
Fast and Accurate Reconstruction of Compressed Color Light Field
Ofir Nabati, David Mendlovic, Raja Giryes
Light field photography has been studied thoroughly in recent years. One of its drawbacks is the need for multi-lens in the imaging. To compensate that, compressed light field phot…
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
Eli Schwartz, Raja Giryes, Alex M. Bronstein
We present DeepISP, a full end-to-end deep neural model of the camera image signal processing (ISP) pipeline. Our model learns a mapping from the raw low-light mosaiced image to th…