24 citations · 60 across the 5 of their papers we have counts for
5 papers
Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions
Touqeer Ahmad, Mohsen Jafarzadeh, Akshay Raj Dhamija +4
There exists a distribution discrepancy between training and testing, in the way images are fed to modern CNNs. Recent work tried to bridge this gap either by fine-tuning or re-tra…
Self-Supervised Features Improve Open-World Learning
Akshay Raj Dhamija, Touqeer Ahmad, Jonathan Schwan +3
This paper identifies the flaws in existing open-world learning approaches and attempts to provide a complete picture in the form of \textbf{True Open-World Learning}. We accomplis…
Automatic Open-World Reliability Assessment
Mohsen Jafarzadeh, Touqeer Ahmad, Akshay Raj Dhamija +3
Image classification in the open-world must handle out-of-distribution (OOD) images. Systems should ideally reject OOD images, or they will map atop of known classes and reduce rel…
A Unifying Framework for Formal Theories of Novelty:Framework, Examples and Discussion
T. E. Boult, P. A. Grabowicz, D. S. Prijatelj +11
Managing inputs that are novel, unknown, or out-of-distribution is critical as an agent moves from the lab to the open world. Novelty-related problems include being tolerant to nov…
A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels
Mohsen Jafarzadeh, Akshay Raj Dhamija, Steve Cruz +3
In open-world learning, an agent starts with a set of known classes, detects, and manages things that it does not know, and learns them over time from a non-stationary stream of da…