57 citations · 112 across the 12 of their papers we have counts for
13 papers
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
ShengYun Peng, Weilin Xu, Cory Cornelius +6
Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs. To accomplish our goal, we synthesize a sui…
RobArch: Designing Robust Architectures against Adversarial Attacks
ShengYun Peng, Weilin Xu, Cory Cornelius +4
Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs). However, compared to the large body of research in optimizing the a…
IMB-NAS: Neural Architecture Search for Imbalanced Datasets
Rahul Duggal, Shengyun Peng, Hao Zhou +1
Class imbalance is a ubiquitous phenomenon occurring in real world data distributions. To overcome its detrimental effect on training accurate classifiers, existing work follows th…
Towards Regression-Free Neural Networks for Diverse Compute Platforms
Rahul Duggal, Hao Zhou, Shuo Yang +3
With the shift towards on-device deep learning, ensuring a consistent behavior of an AI service across diverse compute platforms becomes tremendously important. Our work tackles th…
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
Haekyu Park, Seongmin Lee, Benjamin Hoover +7
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training. Our work add…
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…
Compatibility-aware Heterogeneous Visual Search
Rahul Duggal, Hao Zhou, Shuo Yang +4
We tackle the problem of visual search under resource constraints. Existing systems use the same embedding model to compute representations (embeddings) for the query and gallery i…
MalNet: A Large-Scale Image Database of Malicious Software
Scott Freitas, Rahul Duggal, Duen Horng Chau
Computer vision is playing an increasingly important role in automated malware detection with the rise of the image-based binary representation. These binary images are fast to gen…
ELF: An Early-Exiting Framework for Long-Tailed Classification
Rahul Duggal, Scott Freitas, Sunny Dhamnani +2
The natural world often follows a long-tailed data distribution where only a few classes account for most of the examples. This long-tail causes classifiers to overfit to the major…
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
Rahul Duggal, Scott Freitas, Cao Xiao +2
In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep lear…
CUP: Cluster Pruning for Compressing Deep Neural Networks
Rahul Duggal, Cao Xiao, Richard Vuduc +1
We propose Cluster Pruning (CUP) for compressing and accelerating deep neural networks. Our approach prunes similar filters by clustering them based on features derived from both t…
2017 Robotic Instrument Segmentation Challenge
Max Allan, Alex Shvets, Thomas Kurmann +16
In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand t…
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network
Naushad Ansari, Anubha Gupta, Rahul Duggal
Image-matched nonseparable wavelets can find potential use in many applications including image classification, segmen- tation, compressive sensing, etc. This paper proposes a nove…