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PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data
The efficient sampling in the Gaussian latent space allows our model to perform uncertainty quantification for the super-resolved results.
IDLat: An Importance-Driven Latent Generation Method for Scientific Data
We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.
An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks
Despite the great success of Convolutional Neural Networks (CNNs) in Computer Vision and Natural Language Processing, the working mechanism behind CNNs is still under extensive discussions and research.
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