Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications.
The primary goal of skeletal motion prediction is to generate future motion by observing a sequence of 3D skeletons.
However, while the results are desirable, finding the best compression strategy for a given neural network, target platform, and optimization objective often requires extensive experimentation.
Unsupervised domain adaptive object detection aims to learn a robust detector in the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different.
In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable.
This paper studies the addition of linear constraints to the Support Vector Regression (SVR) when the kernel is linear.
The success of image perturbations that are designed to fool image classification is assessed in terms of both adversarial effect and visual imperceptibility.
Tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming.
The system relies on multi-planar data augmentation which facilitates the application of a single 2D architecture based on the familiar U-Net.