26 citations · 30 across the 10 of their papers we have counts for
6 papers · 1 filter
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning
Nilaksh Das, Sheng-Yun Peng, Duen Horng Chau
Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics. However, the growing threat of advers…
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Haekyu Park, Nilaksh Das, Rahul Duggal +4
Existing research on making sense of deep neural networks often focuses on neuron-level interpretation, which may not adequately capture the bigger picture of how concepts are coll…
SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models
Haekyu Park, Zijie J. Wang, Nilaksh Das +4
Skeleton-based human action recognition technologies are increasingly used in video based applications, such as home robotics, healthcare on aging population, and surveillance. How…
GOGGLES: Automatic Image Labeling with Affinity Coding
Nilaksh Das, Sanya Chaba, Renzhi Wu +3
Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, the data programming paradigm has…
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen +5
The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. This…
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen +4
Deep neural networks (DNNs) have achieved great success in solving a variety of machine learning (ML) problems, especially in the domain of image recognition. However, recent resea…