3 citations · 4 across the 4 of their papers we have counts for
4 papers
A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning Tasks
Samuel Hess, Gregory Ditzler
Few-shot learning is a rapidly evolving area of research in machine learning where the goal is to classify unlabeled data with only one or "a few" labeled exemplary samples. Neural…
Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks
Andrew Wang, Wyatt Mayor, Ryan Smith +2
Robust classification is essential in tasks like autonomous vehicle sign recognition, where the downsides of misclassification can be grave. Adversarial attacks threaten the robust…
Adversarial Filters for Secure Modulation Classification
Alex Berian, Kory Staab, Noel Teku +3
Modulation Classification (MC) refers to the problem of classifying the modulation class of a wireless signal. In the wireless communications pipeline, MC is the first operation pe…
Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth Estimation
Kuo-Shiuan Peng, Gregory Ditzler, Jerzy Rozenblit
Self-supervised monocular depth estimation methods generally suffer the occlusion fading issue due to the lack of supervision by the per pixel ground truth. Although a post-process…