Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers. - Nathan Inkawhich; Matthew Inkawhich; Yiran Chen 001; Hai Li 001 (CoRR, 2018)

Feature Space Perturbations Yield More Transferable Adversarial Examples. - Nathan Inkawhich; Wei Wen; Hai (Helen) Li; Yiran Chen 001 (CVPR, 2019)

Transferable Perturbations of Deep Feature Distributions. - Nathan Inkawhich; Kevin J. Liang; Lawrence Carin; Yiran Chen 001 (ICLR, 2020)

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability. - Nathan Inkawhich; Kevin J. Liang; Binghui Wang; Matthew Inkawhich; Lawrence Carin; Yiran Chen 001 (NeurIPS, 2020)

DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles. - Huanrui Yang; Jingyang Zhang; Hongliang Dong; Nathan Inkawhich; Andrew Gardner; Andrew Touchet; Wesley Wilkes; Heath Berry; Hai Li 001 (NeurIPS, 2020)

NTIRE 2021 Multi-Modal Aerial View Object Classification Challenge. - Jerrick Liu; Nathan Inkawhich; Oliver Nina; Radu Timofte (CVPR Workshops, 2021)

Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap? - Nathan Inkawhich; Kevin J. Liang; Jingyang Zhang; Huanrui Yang; Hai Li 001; Yiran Chen 001 (ICCVW, 2021)

Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure. - Jingyang Zhang; Nathan Inkawhich; Yiran Chen 001; Hai Li 001 (CoRR, 2021)

Training SAR-ATR Models for Reliable Operation in Open-World Environments. - Nathan Inkawhich; Eric K. Davis; Matthew Inkawhich; Uttam K. Majumder; Yiran Chen 001 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021)

Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data. - Nathan Inkawhich; Matthew Inkawhich; Eric K. Davis; Uttam K. Majumder; Erin E. Tripp; Chris Capraro; Yiran Chen 001 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021)

Advancing the Design and Utility of Adversarial Machine Learning Methods. - Nathan Inkawhich (2021)

Multi-modal Aerial View Object Classification Challenge Results - PBVS 2022. - Spencer Low; Oliver Nina; Ángel D. Sappa; Erik Blasch; Nathan Inkawhich (CVPR Workshops, 2022)

The Untapped Potential of Off-the-Shelf Convolutional Neural Networks. - Matthew Inkawhich; Nathan Inkawhich; Eric K. Davis; Hai Li 001; Yiran Chen 001 (WACV, 2022)

Self-Trained Proposal Networks for the Open World. - Matthew Inkawhich; Nathan Inkawhich; Hai Helen Li; Yiran Chen 001 (CoRR, 2022)

Improving Out-of-Distribution Detection by Learning From the Deployment Environment. - Nathan Inkawhich; Jingyang Zhang; Eric K. Davis; Ryan Luley; Yiran Chen 001 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022)

Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification. - Randolph Linderman; Jingyang Zhang; Nathan Inkawhich; Hai Helen Li; Yiran Chen 001 (CoLLAs, 2023)

Multi-modal Aerial View Object Classification Challenge Results - PBVS 2023. - Spencer Low; Oliver Nina; Angel Domingo Sappa; Erik Blasch; Nathan Inkawhich (CVPR Workshops, 2023)

Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023. - Spencer Low; Oliver Nina; Angel Domingo Sappa; Erik Blasch; Nathan Inkawhich (CVPR Workshops, 2023)

Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments. - Jingyang Zhang; Nathan Inkawhich; Randolph Linderman; Yiran Chen 001; Hai Li 001 (WACV, 2023)

SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection. - Jingyang Zhang; Nathan Inkawhich; Randolph Linderman; Ryan Luley; Yiran Chen 001; Hai Helen Li (CoRR, 2023)

Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection. - Noah Fleischmann; Walter D. Bennette; Nathan Inkawhich (CoRR, 2023)

Adversarial Attacks on Foundational Vision Models. - Nathan Inkawhich; Gwendolyn McDonald; Ryan Luley (CoRR, 2023)

A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition. - Nathan Inkawhich (IEEE Geosci. Remote. Sens. Lett., 2023)

OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery. - Matthew Inkawhich; Nathan Inkawhich; Hao Yang 018; Jingyang Zhang; Randolph Linderman; Yiran Chen 001 (IEEE Big Data, 2024)

Multi-modal Aerial View Image Challenge: Sensor Domain Translation. - Spencer Low; Oliver Nina; Dylan Bowald; Angel Domingo Sappa; Nathan Inkawhich; Peter Bruns (CVPR Workshops, 2024)

Multi-modal Aerial View Image Challenge: SAR Classification. - Spencer Low; Oliver Nina; Dylan Bowald; Angel Domingo Sappa; Nathan Inkawhich; Peter Bruns (CVPR Workshops, 2024)

Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble. - Chenhui Xu; Fuxun Yu; Zirui Xu; Nathan Inkawhich; Xiang Chen 010 (ICML, 2024)

SoK: A Review of Differentially Private Linear Models For High-Dimensional Data. - Amol Khanna; Edward Raff; Nathan Inkawhich (SaTML, 2024)

Tunable Hybrid Proposal Networks for the Open World. - Matthew Inkawhich; Nathan Inkawhich; Hai Li 001; Yiran Chen 001 (WACV, 2024)

Comprehensive OOD Detection Improvements. - Anish Lakkapragada; Amol Khanna; Edward Raff; Nathan Inkawhich (CoRR, 2024)

3rd Multi-modal Aerial View Image Challenge: Sensor Domain Translation - PBVS 2025. - Dylan Bowald; Justice Wheelwright; Oliver Nina; Ángel D. Sappa; Riad I. Hammoud; Erik Blasch; Nathan Inkawhich (CVPR Workshops, 2025)

4th Multi-modal Aerial View Image Challenge: SAR Classification - PBVS 2025. - Nathan Inkawhich; Claire Thorp; Justice Wheelwright; Oliver Nina; Dylan Bowald; Ángel D. Sappa; Erik Blasch (CVPR Workshops, 2025)

Multi-layer Radial Basis Function Networks for Out-of-distribution Detection. - Amol Khanna; Chenyi Ling; Derek Everett; Edward Raff; Nathan Inkawhich (CVPR Workshops, 2025)