ArticleEntropy (Basel, Switzerland)2023
Deep Classification with Linearity-Enhanced Logits to Softmax Function.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Enhancing crack detection and severity assessment in historical Tabiya basins using U-Net and adaptive thresholding.Frontiers in artificial intelligence · 2026Article
- Advanced vision transformers and open-set learning for robust mosquito classification: A novel approach to entomological studies.PLoS computational biology · 2024Article
- Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models.Medicina (Kaunas, Lithuania) · 2024Review
- Machine vision-based autonomous road hazard avoidance system for self-driving vehicles.Scientific reports · 2024Article
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.