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Class-rebalanced wasserstein distance for multi-source domain adaptation
In the study of machine learning, multi-source domain adaptation (MSDA) handles multiple datasets which are collected from different distributions by...
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MHA-WoML: Multi-head attention and Wasserstein-OT for few-shot learning
Few-shot learning aims to classify novel classes with extreme few labeled samples. Existing metric-learning-based approaches tend to employ the...
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Stable parallel training of Wasserstein conditional generative adversarial neural networks
We propose a stable, parallel approach to train Wasserstein conditional generative adversarial neural networks (W-CGANs) under the constraint of a...
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DC-GAN with feature attention for single image dehazing
In recent years, the frequent occurrence of smog weather has affected people’s health and has also had a major impact on computer vision application...
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Single Image Inpainting Method Using Wasserstein Generative Adversarial Networks and Self-attention
Due to various factors, some parts of images can be lost. Recovering the damaged regions of images is essential. In this paper, a single image...
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A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging
Learning based single image super resolution (SISR) is well investigated in 2D images. However, SISR for 3D magnetic resonance images (MRI) is more...
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PEDI-GAN: power equipment data imputation based on generative adversarial networks with auxiliary encoder
Smart grids commonly rely on analyzing sensor data to monitor power equipment. However, these sensor data can suffer varying levels of loss or...
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CondTraj-GAN: Conditional Sequential GAN for Generating Synthetic Vehicle Trajectories
While the ever-increasing amount of available data has enabled complex machine learning algorithms in various application areas, maintaining data...
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SAC-GAN: Face Image Inpainting with Spatial-Aware Attribute Controllable GAN
The objective of image inpainting is refilling the masked area with semantically appropriate pixels and producing visually realistic images as an...
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EVGAN: Optimization of Generative Adversarial Networks Using Wasserstein Distance and Neuroevolution
Generative Adversarial Networks (or called GANs) is a generative type of model which can be used to generate new data points from the given initial...
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Wasserstein distance based multi-scale adversarial domain adaptation method for remaining useful life prediction
Accurate remaining useful life (RUL) prediction can formulate timely maintenance strategies for mechanical equipment and reduce the costs of...
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Opinion mining from amazon reviews using dual interactive wasserstein namib beetle generative adversarial network
Sentimental analysis, is the study of sentiments which resolves the judgement of customer’s opinions emotions, sentiments and evaluations in terms of...
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Smart GAN: a smart generative adversarial network for limited imbalanced dataset
Advancements in Machine Learning (ML) and Computer Vision have led to notable improvements in the detection of breast cancer. However, the accuracy...
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Generating Emotional Music Based on Improved C-RNN-GAN
This study introduces an emotion-based music generation model built upon the foundation of C-RNN-GAN, incorporating conditional GAN, and utilizing...
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SSGAN: A Semantic Similarity-Based GAN for Small-Sample Image Augmentation
Image sample augmentation refers to strategies for increasing sample size by modifying current data or synthesizing new data based on existing data....
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Handwriting Recognition Using Wasserstein Metric in Adversarial Learning
Deep intelligence provides a great way to deal with understanding the complex handwriting of the user. Handwriting is challenging due to its...
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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks
Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There...
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LUCID–GAN: Conditional Generative Models to Locate Unfairness
Most group fairness notions detect unethical biases by computing statistical parity metrics on a model’s output. However, this approach suffers from...
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GAN-Driven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical Imaging
In the biomedical imaging domain, large preprocessed samples of training annotated images are required in techniques employing neural networks for...
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SpotGAN: A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization
Generating molecules with a given scaffold is a challenging task in drug-discovery. Scaffolds impose strict constraints on the generation of...