Cargando…
Direct domain adaptation through reciprocal linear transformations
We propose a direct domain adaptation (DDA) approach to enrich the training of supervised neural networks on synthetic data by features from real-world data. The process involves a series of linear operations on the input features to the NN model, whether they are from the source or target distribut...
Autores principales: | Alkhalifah, Tariq, Ovcharenko, Oleg |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402934/ https://www.ncbi.nlm.nih.gov/pubmed/36034594 http://dx.doi.org/10.3389/frai.2022.927676 |
Ejemplares similares
-
Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images
por: Thiam, Patrick, et al.
Publicado: (2023) -
Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting
por: Mücher, Christian
Publicado: (2022) -
Credit Risk Modeling Using Transfer Learning and Domain Adaptation
por: Suryanto, Hendra, et al.
Publicado: (2022) -
Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit
por: Zhu, Yuanda, et al.
Publicado: (2022) -
GDP Forecasting: Machine Learning, Linear or Autoregression?
por: Maccarrone, Giovanni, et al.
Publicado: (2021)