Search Results for author:
Found 6 papers, 4 papers with code
Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions
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
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}.
A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels
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.
Automatic Open-World Reliability Assessment
However, during operations, we cannot directly assess accuracy as there are no labels.
Reducing Network Agnostophobia
Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications.
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Face detection and recognition benchmarks have shifted toward more difficult environments.
