21 citations · 25 across the 4 of their papers we have counts for
7 papers
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Cheng Peng, Pengfei Guo, S. Kevin Zhou +2
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time. To this end, current State-of-The-Art (SoTA) approaches leverage deep neu…
Self-Denoising Neural Networks for Few Shot Learning
Steven Schwarcz, Sai Saketh Rambhatla, Rama Chellappa
In this paper, we introduce a new architecture for few shot learning, the task of teaching a neural network from as few as one or five labeled examples. Inspired by the theoretical…
The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory
Sai Saketh Rambhatla, Rama Chellappa, Abhinav Shrivastava
We tackle object category discovery, which is the problem of discovering and localizing novel objects in a large unlabeled dataset. While existing methods show results on datasets…
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
Yogesh Balaji, Rama Chellappa, Soheil Feizi
Optimal Transport (OT) distances such as Wasserstein have been used in several areas such as GANs and domain adaptation. OT, however, is very sensitive to outliers (samples with la…
Pose And Joint-Aware Action Recognition
Anshul Shah, Shlok Mishra, Ankan Bansal +3
Recent progress on action recognition has mainly focused on RGB and optical flow features. In this paper, we approach the problem of joint-based action recognition. Unlike other mo…
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue
Pirazh Khorramshahi, Hossein Souri, Rama Chellappa +1
Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs are often…