Synthetic Data for Deep Learning in Computer Vision & Medical Imaging: A Means to Reduce Data Bias

A Paproki, O Salvado, C Fookes - ACM Computing Surveys, 2024 - dl.acm.org
Deep-learning (DL) performs well in computer-vision and medical-imaging automated
decision-making applications. A bottleneck of DL stems from the large amount of labelled …

Fairfacegan: Fairness-aware facial image-to-image translation

S Hwang, S Park, D Kim, M Do, H Byun - ar** function that
preserves the semantics of an input image while adapting its style to target domains without …

Smile: Semantically-guided multi-attribute image and layout editing

A Romero, L Van Gool… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Attribute image manipulation has been a very active topic since the introduction of
Generative Adversarial Networks (GANs). Exploring the disentangled attribute space within …

Separating content and style for unsupervised image-to-image translation

Y Liu, H Wang, Y Yue, F Lu - ar** between two visual
domains with unpaired samples. Existing works focus on disentangling domain-invariant …

[BOK][B] Debiasing Image Generative Models

MM Tanjim - 2023 - search.proquest.com
Generative models have become increasingly popular in various domains to solve
challenging tasks, including image generation, dialogue generation, and story generation …

Transferring and learning representations for image generation and translation

Y Wang - 2020 - ddd.uab.cat
Image generation is arguably one of the most attractive, compelling, and challenging tasks
in computer vision. Among the methods which perform image generation, generative …