Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions

no code implementations • 19 Jan 2022 • Touqeer Ahmad, Mohsen Jafarzadeh, Akshay Raj Dhamija, Ryan Rabinowitz, Steve Cruz, Chunchun Li, Terrance E. Boult

Specifically, we demonstrate that running inference on the center crop of an image is not always the best as important discriminatory information may be cropped-off.

Self-Supervised Features Improve Open-World Learning

1 code implementation • 15 Feb 2021 • Akshay Raj Dhamija, Touqeer Ahmad, Jonathan Schwan, Mohsen Jafarzadeh, Chunchun Li, Terrance E. Boult

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}.

Incremental Learning Out-of-Distribution Detection

A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels

1 code implementation • 25 Nov 2020 • Mohsen Jafarzadeh, Akshay Raj Dhamija, Steve Cruz, Chunchun Li, Touqeer Ahmad, Terrance E. Boult

Open-world learning is related to but also distinct from a multitude of other learning problems and this paper briefly analyzes the key differences between a wide range of problems including incremental learning, generalized novelty discovery, and generalized zero-shot learning.

Generalized Zero-Shot Learning Image Classification +3

Automatic Open-World Reliability Assessment

1 code implementation • 11 Nov 2020 • Mohsen Jafarzadeh, Touqeer Ahmad, Akshay Raj Dhamija, Chunchun Li, Steve Cruz, Terrance E. Boult

However, during operations, we cannot directly assess accuracy as there are no labels.

Image Classification