Nov 15, 2021 · This paper proposes a novel synthetic unknown class learning method that generates unknown-like samples while maintaining diversity between the generated ...
This paper proposes a novel synthetic unknown class learning method that constantly generates unknown-like samples while maintaining diversity between the ...
This paper addresses the open set recognition (OSR) problem, where the goal is to correctly classify samples of known classes while detecting unknown ...
Synthetic Unknown Class Learning for Learning Unknowns · Jaeyeon Jang · Published in Pattern Recognition 15 November 2021 · Computer Science.
This paper addresses the open set recognition (OSR) problem, where the goal is to correctly classify samples of known classes while detecting unknown ...
May 12, 2024 · By learning the unknown-like samples and known samples in an alternating manner, the proposed method can not only experience diverse synthetic ...
Nov 15, 2021 · Thus, this paper proposes a novel synthetic unknown class learning method that generates unknown-like samples while maintaining diversity ...
Synthetic unknown class learning for learning unknowns. https://doi.org/10.1016/j.patcog.2024.110560 ·. Journal: Pattern Recognition, 2024, p. 110560.
Jul 19, 2020 · A solution to this would be to classify a datapoint as "Unknown" instead of a complete misclassification (these unknowns could then be human-classified)
Missing: Synthetic | Show results with:Synthetic
Mar 1, 2018 · It is true that neural networks are inherently not good at classifying 'unknowns' because they tend to overfit to the data that they have been trained on.
Missing: Synthetic | Show results with:Synthetic