Source-free domain adaptation (SFDA) is a popular unsupervised domain adaptation method where a pre-trained model from a source domain is adapted to a target domain without accessing any source data. Despite rich results in this area, existing literature overlooks the security challenges of the unsupervised SFDA setting in presence of a malicious source domain owner. This work investigates the eff...Show More
The concept of weight pruning has shown success in neural network model compression with marginal loss in classification performance. However, similar concepts have not been well recognized in improving unsupervised learning. To the best of our knowledge, this paper proposes one of the first studies on weight pruning in unsupervised autoencoder models using non-imaging data points. We adapt the we...Show More
Three-dimensional (3D) imaging provides detailed geometry of real-world objects, unlike 2D image texture. The rudimentary form of 3D imaging is point clouds that are distinctly different from image pixels in terms of structure and processing methods. The 3D computer vision literature primarily retrieves global shape patterns in 3D data for object and face recognition tasks. In contrast, mining loc...Show More
