82 citations · 178 across the 7 of their papers we have counts for
7 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…
Reducing Network Agnostophobia
Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult
Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications. Unfortunately, network behavior is n…