289 citations · 776 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…