most citedAutomatic Head Overcoat Thickness Measure with NASNet-Large-Decoder Net

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cs.CV2022

Memory Defense: More Robust Classification via a Memory-Masking Autoencoder

Eashan Adhikarla, Dan Luo, Brian D. Davison

Many deep neural networks are susceptible to minute perturbations of images that have been carefully crafted to cause misclassification. Ideally, a robust classifier would be immun…

cs.CV20211 cited

Automatic Head Overcoat Thickness Measure with NASNet-Large-Decoder Net

Youshan Zhang, Brian D. Davison, Vivien W. Talghader +3

Transmission electron microscopy (TEM) is one of the primary tools to show microstructural characterization of materials as well as film thickness. However, manual determination of…

cs.CV2021

Enhanced Separable Disentanglement for Unsupervised Domain Adaptation

Youshan Zhang, Brian D. Davison

Domain adaptation aims to mitigate the domain gap when transferring knowledge from an existing labeled domain to a new domain. However, existing disentanglement-based methods do no…

cs.CV2021

Correlated Adversarial Joint Discrepancy Adaptation Network

Youshan Zhang, Brian D. Davison

Domain adaptation aims to mitigate the domain shift problem when transferring knowledge from one domain into another similar but different domain. However, most existing works rely…

cs.CV2021

Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation

Youshan Zhang, Brian D. Davison

Unsupervised Domain adaptation is an effective method in addressing the domain shift issue when transferring knowledge from an existing richly labeled domain to a new domain. Exist…

cs.CV2021

Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation

Youshan Zhang, Brian D. Davison

Domain adaptation (DA) mitigates the domain shift problem when transferring knowledge from one annotated domain to another similar but different unlabeled domain. However, existing…