7 citations · 9 across the 4 of their papers we have counts for
5 papers
Probing the Purview of Neural Networks via Gradient Analysis
Jinsol Lee, Charlie Lehman, Mohit Prabhushankar +1
We analyze the data-dependent capacity of neural networks and assess anomalies in inputs from the perspective of networks during inference. The notion of data-dependent capacity al…
Gradient-Based Adversarial and Out-of-Distribution Detection
Jinsol Lee, Mohit Prabhushankar, Ghassan AlRegib
We propose to utilize gradients for detecting adversarial and out-of-distribution samples. We introduce confounding labels -- labels that differ from normal labels seen during trai…
Gradients as a Measure of Uncertainty in Neural Networks
Jinsol Lee, Ghassan AlRegib
Despite tremendous success of modern neural networks, they are known to be overconfident even when the model encounters inputs with unfamiliar conditions. Detecting such inputs is…
Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance Estimation
Dogancan Temel, Jinsol Lee, Ghassan AlRegib
In this paper, we investigate the reliability of online recognition platforms, Amazon Rekognition and Microsoft Azure, with respect to changes in background, acquisition device, an…
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
Dogancan Temel, Jinsol Lee, Ghassan AlRegib
In this paper, we introduce a large-scale, controlled, and multi-platform object recognition dataset denoted as Challenging Unreal and Real Environments for Object Recognition (CUR…