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A review of generalized zero-shot learning methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …
under the condition that some output classes are unknown during supervised learning. To …
A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
UncertaintyFuseNet: robust uncertainty-aware hierarchical feature fusion model with ensemble Monte Carlo dropout for COVID-19 detection
Abstract The COVID-19 (Coronavirus disease 2019) pandemic has become a major global
threat to human health and well-being. Thus, the development of computer-aided detection …
threat to human health and well-being. Thus, the development of computer-aided detection …
BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification
Automatic medical image analysis (eg, medical image classification) is widely used in the
early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable …
early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable …
[HTML][HTML] Deep convolutional generative adversarial networks to enhance artificial intelligence in healthcare: a skin cancer application
In recent years, researchers designed several artificial intelligence solutions for healthcare
applications, which usually evolved into functional solutions for clinical practice …
applications, which usually evolved into functional solutions for clinical practice …
Generalized zero-shot learning for classifying unseen wafer map patterns
A data-driven approach for classifying defect patterns in wafer maps is crucial for automating
the quality assurance process during semiconductor fabrication. Existing works have …
the quality assurance process during semiconductor fabrication. Existing works have …
Generative adversarial network to alleviate information insufficiency in intelligent fault diagnosis by generating continuations of signals
Z Dai, L Zhao, K Wang, Y Zhou - Applied Soft Computing, 2023 - Elsevier
This paper introduces Con-GAN, an innovative improvement of GAN-based data
augmentation designed to address data insufficiency in fault diagnosis methodologies …
augmentation designed to address data insufficiency in fault diagnosis methodologies …
[HTML][HTML] Contrastive semantic disentanglement in latent space for generalized zero-shot learning
The target of generalized zero-shot learning (GZSL) is to train a model that can classify data
samples from both seen categories and unseen categories under the circumstances that …
samples from both seen categories and unseen categories under the circumstances that …
A GAN-based method for diagnosing bodywork spot welding defects in response to small sample condition
C Geng, S Buyun, F Gaocai, C **angxiang… - Applied Soft …, 2024 - Elsevier
Due to the hidden nature and complexity of resistance spot welding weld nugget formation,
how to avoid the time-consuming and money-consuming problem of traditional defect …
how to avoid the time-consuming and money-consuming problem of traditional defect …
Leveraging dual variational autoencoders and generative adversarial networks for enhanced multimodal interaction in zero-shot learning
N Li, J Chen, N Fu, W **ao, T Ye, C Gao, P Zhang - Electronics, 2024 - mdpi.com
In the evolving field of taxonomic classification, and especially in Zero-shot Learning (ZSL),
the challenge of accurately classifying entities unseen in training datasets remains a …
the challenge of accurately classifying entities unseen in training datasets remains a …