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Found 5 papers, 0 papers with code
Context-Aware Design of Cyber-Physical Human Systems (CPHS)
The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models.
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters
We show that by learning the features at each resolution independently a trained model is able to accurately classify characters even in the presence of noise.
Ranked #1 on
Image Classification
on Noisy MNIST (AWGN)
Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs)
To potentially reduce the discrepancies and improve the prediction accuracy of BPMs, this paper proposes a computational framework for learning mixture models by using Generative Adversarial Networks (GANs) that appropriately combining existing BPMs with knowledge on occupant behaviors to contextual factors in new designs.
Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images
Machine learning has proven to be useful in classification and segmentation of images.
CactusNets: Layer Applicability as a Metric for Transfer Learning
Deep neural networks trained over large datasets learn features that are both generic to the whole dataset, and specific to individual classes in the dataset.
