37 citations · 46 across the 8 of their papers we have counts for
10 papers
FRE: A Fast Method For Anomaly Detection And Segmentation
Ibrahima Ndiour, Nilesh Ahuja, Utku Genc +1
This paper presents a fast and principled approach for solving the visual anomaly detection and segmentation problem. In this setup, we have access to only anomaly-free training da…
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection
Ibrahima J. Ndiour, Nilesh A. Ahuja, Omesh Tickoo
This paper presents a fast, principled approach for detecting anomalous and out-of-distribution (OOD) samples in deep neural networks (DNN). We propose the application of linear st…
Anomalib: A Deep Learning Library for Anomaly Detection
Samet Akcay, Dick Ameln, Ashwin Vaidya +3
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides…
Mitigating Sampling Bias and Improving Robustness in Active Learning
Ranganath Krishnan, Alok Sinha, Nilesh Ahuja +3
This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness. We introduce supervise…
Energy-Based Anomaly Detection and Localization
Ergin Utku Genc, Nilesh Ahuja, Ibrahima J Ndiour +1
This brief sketches initial progress towards a unified energy-based solution for the semi-supervised visual anomaly detection and localization problem. In this setup, we have acces…
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features
Ibrahima Ndiour, Nilesh Ahuja, Omesh Tickoo
This paper presents a principled approach for detecting out-of-distribution (OOD) samples in deep neural networks (DNN). Modeling probability distributions on deep features has rec…