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Found 7 papers, 1 papers with code
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images
In this paper, both varieties of the mode collapse problem are investigated, and their subsequent impact on the diversity of synthetic X-ray images is evaluated.
Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting
Biomedical image features are sensitive to evaluating the efficacy of synthetic images.
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis
The proposed architecture is compared with the multi-scale gradient GAN (MSG-GAN) to assess the diversity of generated synthetic images.
Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery
Furthermore, the metrics' capacity to indicate the quality and diversity of synthetic images and a correlation with classifier performance is undertaken.
Classification of Stress via Ambulatory ECG and GSR Data
This work empirically assesses several approaches utilising machine learning classifiers to detect stress using physiological data recorded in an ambulatory setting with self-reported stress annotations.
Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Images
It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery.
A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis
This is due to deep learning models requiring large image datasets to provide high-level performance.
