An Example for Domain Adaptation Using CycleGAN
Cycle-Consistent Adversarial Network (CycleGAN) is very promising in domain adaptation. In this report, an example in medical domain will be explained. We present struecture of a CycleGAN model for unpaired image-to-image translation from microscopy to pseudo H\&E stained histopathology images.
Code (0)
등록된 구현이 없습니다.
Tasks
Image-to-Image TranslationDomain AdaptationSimilar Papers 제목 키워드 기반
Deep Feature CycleGANs: Speaker Identity Preserving Non-parallel Microphone-Telephone Domain Adaptation for Speaker Verification
With the increase in the availability of speech from varied domains, it is imperative to use such out-of-domain data to improve existing speech systems. Domain adaptation is a prominent pre-processing approach for this. …
Domain AdaptationSpeaker VerificationTranslationMTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry
In the current era, an increasing number of machine learning models is generated for the automation of industrial processes. To that end, machine learning models are trained using historical data of each single asset lea…
BIG-bench Machine LearningDomain AdaptationTime SeriesTime Series AnalysisCurriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Sentiment analysis of user-generated reviews or comments on products and services in social networks can help enterprises to analyze the feedback from customers and take corresponding actions for improvement. To mitigate…
Domain AdaptationGenerative Adversarial NetworkSentiment AnalysisSentiment ClassificationNonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs
Neural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs. Although these methods achieve highly accurate super-resolution if the acoustic characteristics of the input data…
Audio Super-ResolutionDomain AdaptationSuper-ResolutionUnsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformations captured by CycleGAN make the synthe…
Image Generation