ArticleFrontiers in robotics and AI2018
RenderGAN: Generating Realistic Labeled Data.
Article in Frontiers in robotics and AI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Machine learning reveals the waggle drift's role in the honey bee dance communication system.PNAS nexus · 2023Article
- Small object intelligent detection method based on adaptive recursive feature pyramid.Heliyon · 2023Article
- Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development.Journal of cheminformatics · 2021Article
- Generative adversarial networks for imputing missing data for big data clinical research.BMC medical research methodology · 2021Observational
- A deep learning approach to identify unhealthy advertisements in street view images.Scientific reports · 2021Article
- An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture.PloS one · 2020Article
- Learning to Compose Domain-Specific Transformations for Data Augmentation.Advances in neural information processing systems · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g., lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.