Deep Classification with Linearity-Enhanced Logits to Softmax Function.
Entropy (Basel)
; 25(5)2023 Apr 27.
Article
en En
| MEDLINE
| ID: mdl-37238482
ABSTRACT
Recently, there has been a rapid increase in deep classification tasks, such as image recognition and target detection. As one of the most crucial components in Convolutional Neural Network (CNN) architectures, softmax arguably encourages CNN to achieve better performance in image recognition. Under this scheme, we present a conceptually intuitive learning objection function Orthogonal-Softmax. The primary property of the loss function is to use a linear approximation model that is designed by Gram-Schmidt orthogonalization. Firstly, compared with the traditional softmax and Taylor-Softmax, Orthogonal-Softmax has a stronger relationship through orthogonal polynomials expansion. Secondly, a new loss function is advanced to acquire highly discriminative features for classification tasks. At last, we present a linear softmax loss to further promote the intra-class compactness and inter-class discrepancy simultaneously. The results of the widespread experimental discussion on four benchmark datasets manifest the validity of the presented method. Besides, we want to explore the non-ground truth samples in the future.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Entropy (Basel)
Año:
2023
Tipo del documento:
Article
País de afiliación:
China