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Found 6 papers, 1 papers with code
Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks
However, such models face several problems during the learning process, mainly due to the redundancy of the individual neurons, which results in sub-optimal accuracy or the need for additional training steps.
Image Inpainting Using Wasserstein Generative Adversarial Imputation Network
Image inpainting is one of the important tasks in computer vision which focuses on the reconstruction of missing regions in an image.
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
Missing data is one of the most common preprocessing problems.
Unsupervised Latent Space Translation Network
One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation.
Constructing a Data Visualization Recommender System
We then use this guide to create a model for a data visualization recommender system for non-experts that aims to resolve the issues of current solutions.
Missing Features Reconstruction and Its Impact on Classification Accuracy
The imputation impact is researched on a combination of traditional methods such as k-NN, linear regression, and MICE compared to modern imputation methods such as multi-layer perceptron (MLP) and gradient boosted trees (XGBT).
