37 citations · 45 across the 7 of their papers we have counts for
7 papers · 1 filter
Uncertainty Quantification in Continual Open-World Learning
Amanda S. Rios, Ibrahima J. Ndiour, Parual Datta +3
AI deployed in the real-world should be capable of autonomously adapting to novelties encountered after deployment. Yet, in the field of continual learning, the reliance on novelty…
CONCLAD: COntinuous Novel CLAss Detector
Amanda Rios, Ibrahima Ndiour, Parual Datta +2
In the field of continual learning, relying on so-called oracles for novelty detection is commonplace albeit unrealistic. This paper introduces CONCLAD ("COntinuous Novel CLAss Det…
CUAL: Continual Uncertainty-aware Active Learner
Amanda Rios, Ibrahima Ndiour, Parual Datta +3
AI deployed in many real-world use cases should be capable of adapting to novelties encountered after deployment. Here, we consider a challenging, under-explored and realistic cont…
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…
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…