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 …
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 …
A two-stage denoising framework for zero-shot learning with noisy labels
L Tang, P Zhao, Z Pan, X Duan, PM Pardalos - Information Sciences, 2024 - Elsevier
Although zero-shot learning (ZSL) has gained widespread concern due to its excellent
capacity of recognizing new object classes without seeing any visual instances, most …
capacity of recognizing new object classes without seeing any visual instances, most …
Open zero-shot learning via asymmetric VAE with dissimilarity space
Z Zhai, X Li, Z Chang - Information Sciences, 2023 - Elsevier
Abstract Generalized Zero-Shot Learning (GZSL) aims to classify samples from seen and
unseen classes using class-level features. Although existing GZSL methods have achieved …
unseen classes using class-level features. Although existing GZSL methods have achieved …
[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 …
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 …
Integrating adversarial generative network with variational autoencoders towards cross-modal alignment for zero-shot remote sensing image scene classification
S Ma, C Liu, Z Li, W Yang - Remote Sensing, 2022 - mdpi.com
Remote sensing image scene classification takes image blocks as classification units and
predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples …
predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples …