Discriminant Zero-Shot Learning with Center Loss
Sign Up to like & getrecommendations! Published in 2019 at "Cognitive Computation"
DOI: 10.1007/s12559-019-09629-z
Abstract: Current work on zero-shot learning (ZSL) generally does not focus on the discriminative ability of the models, which is important for differentiating between classes since our brain focuses on the discriminating part of the object… read more here.
Keywords: zero shot; discriminant zero; shot learning; center loss ... See more keywords
Gas Sensor Drift Compensation Using Semi-Supervised Ensemble Classifiers with Multi-Level Features and Center Loss.
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DOI: 10.1021/acssensors.4c03655
Abstract: The drift compensation of gas sensors is a significant and challenging issue in the field of electronic noses (E-nose). Compensating sensor drift has a great benefit in improving the performance of E-nose systems. However, conventional… read more here.
Keywords: drift; loss; center loss; gas ... See more keywords
The Effective Evaluation of Emotions in the Visual Emotion Images Using Convolutional Neural Networks
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DOI: 10.1109/access.2025.3596484
Abstract: This paper develops a model for recognizing emotions in visual images. The integration of contrastive-center loss optimization is proposed in this paper. This effectively improves the recognition of emotions when training a convolutional neural network… read more here.
Keywords: convolutional neural; loss; contrastive center; emotions visual ... See more keywords