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Found 6 papers, 1 papers with code
Defense of Word-level Adversarial Attacks via Random Substitution Encoding
The adversarial attacks against deep neural networks on computer vision tasks have spawned many new technologies that help protect models from avoiding false predictions.
Single-shot 3D shape reconstruction using deep convolutional neural networks
A robust single-shot 3D shape reconstruction technique integrating the fringe projection profilometry (FPP) technique with the deep convolutional neural networks (CNNs) is proposed in this letter.
Z-Net: an Anisotropic 3D DCNN for Medical CT Volume Segmentation
Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS).
Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle
3D shape instantiation which reconstructs the 3D shape of a target from limited 2D images or projections is an emerging technique for surgical intervention.
U-Net Training with Instance-Layer Normalization
However, two potential issues exist in BIN: first, the Clip function is not differentiable at input values of 0 and 1; second, the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN.
One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud
An average point cloud-to-point cloud (PC-to-PC) error of 1. 72mm has been achieved, which is comparable to the PLSR-based (1. 42mm) and KPLSR-based (1. 31mm) two-stage shape instantiation algorithm.
