7 accepted papers
Certified adversarial robustness of large-scale deep networks has progressed substantially after the introduction of randomized smoothing. Deep net classifiers are now provably robust in their predictions against a large class of threat models, including $\ell_1$, $\ell_2$, and $\ell_\infty$ norm-bo…
Camera pose estimation has long relied on geometry-based approaches and sparse 2D-3D keypoint correspondences. With the advent of deep learning methods, the estimation of camera pose parameters, i.e., the six parameters that describe position and rotation denoted by 6 Degrees of Freedom (6-DoF), has
In this paper, we generalize the well-known Expectation Maximization (EM) algorithm using the α−divergence for Gaussian Mixture Model (GMM). This approach is used in robust subspace detection when the number of parameters is kept small to avoid overfitting and large estimation variances. The level o…
We studied the problem of robust subspace tracking (RST) in contaminated environments. Leveraging the fast approximated power iteration and α-divergence, a novel robust algorithm called αFAPI was developed for tracking the underlying principal subspace of streaming data over time. αFAPI is fast and…
The problem of detecting a subspace signal in colored Gaussian noise with unknown covariance matrix is investigated when the training data may contain samples with target signal. The target signal is assumed that it lies in a subspace spanned by columns of a known matrix. To develop the test, an ad…
The problem of detecting a subspace signal in the presence of subspace interference and contaminated Gaussian noise with unknown variance is investigated. The target signal is assumed to lie in a subspace spanned by the columns of a known matrix. To develop the test, the same steps used in the gener…
Adaptive subspace detectors (ASD) generalize matched subspace detectors (MSD) by accounting for possible correlation. Both ASD and MSD are derived using the generalized likelihood ratio test (GLRT). While MSD assumes there is no correlation between observations, ASD estimates a sample covariance mat…