341 citations · 1.6k across the 113 of their papers we have counts for
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GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
Minsuk Kahng, Nikhil Thorat, Duen Horng Chau +2
Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive app…
Large Graph Exploration via Subgraph Discovery and Decomposition
James Abello, Fred Hohman, Varun Bezzam +1
We are developing an interactive graph exploration system called Graph Playground for making sense of large graphs. Graph Playground offers a fast and scalable edge decomposition a…
Search Rank Fraud De-Anonymization in Online Systems
Mizanur Rahman, Nestor Hernandez, Bogdan Carbunar +1
We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems. We c…
Interactive Classification for Deep Learning Interpretation
Ángel Alexander Cabrera, Fred Hohman, Jason Lin +1
We present an interactive system enabling users to manipulate images to explore the robustness and sensitivity of deep learning image classifiers. Using modern web technologies to…
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen +3
Adversarial machine learning research has recently demonstrated the feasibility to confuse automatic speech recognition (ASR) models by introducing acoustically imperceptible pertu…
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Shang-Tse Chen, Cory Cornelius, Jason Martin +1
Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) i…
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…
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Fred Hohman, Minsuk Kahng, Robert Pienta +1
Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because o…