Feb 24, 2021 · In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation.
We propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation.
We propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation.
View recent discussion. Abstract: In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation ...
A novel regularization loss suitable for distance-based classifiers is formulated, which reserves sufficiently large class-wise latent feature spaces for ...
In this paper, we propose a new deep neural network classifier that simultaneously 2 maximizes the inter-class separation and minimizes the intra-class ...
Missing: compact | Show results with:compact
Oct 22, 2024 · In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the ...
A lighter (e.g. Polyhedral or ellipsoidal) decision boundary improves this localization, reducing mis-classifications caused by unforeseen classes and outliers.
Deep compact polyhedral conic classifier for open and closed set recognition. https://doi.org/10.1016/j.patcog.2021.108080 ·. Journal: Pattern Recognition, 2021 ...
We introduce a new deep neural network classier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation. • The proposed ...