33 citations · 34 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 1 cited
Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection
Noah Fleischmann, Walter Bennette, Nathan Inkawhich
Machine learning models deployed in the open world may encounter observations that they were not trained to recognize, and they risk misclassifying such observations with high conf…
cs.CV2023
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +3
Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which…
cs.CV2023★ 33 cited
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition
Nathan Inkawhich
In real-world scenarios, it may not always be possible to collect hundreds of labeled samples per class for training deep learning-based SAR Automatic Target Recognition (ATR) mode…