213 citations · 360 across the 5 of their papers we have counts for
5 papers
Robust Convolutional Neural Networks under Adversarial Noise
Jonghoon Jin, Aysegul Dundar, Eugenio Culurciello
Recent studies have shown that Convolutional Neural Networks (CNNs) are vulnerable to a small perturbation of input called "adversarial examples". In this work, we propose a new fe…
Convolutional Clustering for Unsupervised Learning
Aysegul Dundar, Jonghoon Jin, Eugenio Culurciello
The task of labeling data for training deep neural networks is daunting and tedious, requiring millions of labels to achieve the current state-of-the-art results. Such reliance on…
Flattened Convolutional Neural Networks for Feedforward Acceleration
Jonghoon Jin, Aysegul Dundar, Eugenio Culurciello
We present flattened convolutional neural networks that are designed for fast feedforward execution. The redundancy of the parameters, especially weights of the convolutional filte…
An Analysis of the Connections Between Layers of Deep Neural Networks
Eugenio Culurciello, Jonghoon Jin, Aysegul Dundar +1
We present an analysis of different techniques for selecting the connection be- tween layers of deep neural networks. Traditional deep neural networks use ran- dom connection table…
Visual Tracking with Similarity Matching Ratio
Aysegul Dundar, Jonghoon Jin, Eugenio Culurciello
This paper presents a novel approach to visual tracking: Similarity Matching Ratio (SMR). The traditional approach of tracking is minimizing some measures of the difference between…