6 papers
On the Status of Foundation Models for SAR Imagery
Nathan Inkawhich
In this work we investigate the viability of foundational AI/ML models for Synthetic Aperture Radar (SAR) object recognition tasks. We are inspired by the tremendous progress being…
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection
Amol Khanna, Chenyi Ling, Derek Everett +2
Existing methods for out-of-distribution (OOD) detection use various techniques to produce a score, separate from classification, that determines how ``OOD'' an input is. Our insig…
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble
Chenhui Xu, Fuxun Yu, Zirui Xu +2
Recent research underscores the pivotal role of the Out-of-Distribution (OOD) feature representation field scale in determining the efficacy of models in OOD detection. Consequentl…
OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery
Matthew Inkawhich, Nathan Inkawhich, Hao Yang +3
An object detector's ability to detect and flag \textit{novel} objects during open-world deployments is critical for many real-world applications. Unfortunately, much of the work i…
Tunable Hybrid Proposal Networks for the Open World
Matthew Inkawhich, Nathan Inkawhich, Hai Li +1
Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes. These models fail t…
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
Amol Khanna, Edward Raff, Nathan Inkawhich
Linear models are ubiquitous in data science, but are particularly prone to overfitting and data memorization in high dimensions. To guarantee the privacy of training data, differe…