Feb 14, 2022 · In this paper, we focus on enforcing the discriminative power of the high-level representations, that are typically learned by the deeper layers ...

Our study fits in this domain, as our focus is on learning better high-level representations by enforcing their discrim- inability towards the target classes, ...

This paper introduces a new loss term inspired by the Gini impurity, aimed at minimizing the entropy of individual high-level features with respect to the ...

In this paper, we focus on enforcing the discriminative power of the high-level representations, that are typically learned by the deeper layers (closer to the ...

Discriminability-enforcing loss to improve representation learning · 1. Introduction. Learning good data representations is a crucial point to- · 2. Related work.

Sep 9, 2024 · In this paper, we focus on enforcing the discriminative power of the high-level representations, that are typically learned by the deeper layers ...

Discriminability-enforcing loss to improve representation learning ... During the training process, deep neural networks implicitly learn to represent the input ...

Discriminability-enforcing loss to improve representation learning. FA Croitoru, DN Grigore, RT Ionescu. Proceedings of the IEEE/CVF Conference on Computer ...

During the training process, deep neural networks implicitly learn to represent the input data samples through a hierarchy of features, where the size of ...

Discriminability-enforcing loss to improve representation learning. FA Croitoru, DN Grigore, RT Ionescu. Proceedings of the IEEE/CVF Conference on Computer ...