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

Snooping Attacks on Deep Reinforcement Learning. - Matthew Inkawhich; Yiran Chen 001; Hai Helen Li (AAMAS, 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)

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)

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)

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)

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