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08/30/2023
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
Our research aims to unify existing works' diverging opinions on how arc...
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01/08/2023
RobArch: Designing Robust Architectures against Adversarial Attacks
Adversarial Training is the most effective approach for improving the ro...
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09/30/2022
IMB-NAS: Neural Architecture Search for Imbalanced Datasets
Class imbalance is a ubiquitous phenomenon occurring in real world data ...
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09/27/2022
Towards Regression-Free Neural Networks for Diverse Compute Platforms
With the shift towards on-device deep learning, ensuring a consistent be...
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03/30/2022
ConceptEvo: Interpreting Concept Evolution in Deep Learning Training
Deep neural networks (DNNs) have been widely used for decision making, p...
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08/29/2021
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Existing research on making sense of deep neural networks often focuses ...
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05/13/2021
Compatibility-aware Heterogeneous Visual Search
We tackle the problem of visual search under resource constraints. Exist...
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01/31/2021
MalNet: A Large-Scale Cybersecurity Image Database of Malicious Software
Computer vision is playing an increasingly important role in automated m...
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06/22/2020
ELF: An Early-Exiting Framework for Long-Tailed Classification
The natural world often follows a long-tailed data distribution where on...
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01/29/2020
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
In recent years, significant attention has been devoted towards integrat...
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11/19/2019
CUP: Cluster Pruning for Compressing Deep Neural Networks
We propose Cluster Pruning (CUP) for compressing and accelerating deep n...
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02/18/2019
2017 Robotic Instrument Segmentation Challenge
In mainstream computer vision and machine learning, public datasets such...
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12/15/2016