1 code implementation • 7 Dec 2023 • Sharath Girish, Kamal Gupta, Abhinav Shrivastava
Our approach develops a pruning stage which results in scene representations with fewer Gaussians, leading to faster training times and rendering speeds for real-time rendering of high resolution scenes.
no code implementations • ICCV 2023 • Sharath Girish, Abhinav Shrivastava, Kamal Gupta
Implicit Neural Representations (INR) or neural fields have emerged as a popular framework to encode multimedia signals such as images and radiance fields while retaining high-quality.
1 code implementation • CVPR 2023 • Shishira R Maiya, Sharath Girish, Max Ehrlich, Hanyu Wang, Kwot Sin Lee, Patrick Poirson, Pengxiang Wu, Chen Wang, Abhinav Shrivastava
This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video.
1 code implementation • 6 Apr 2022 • Sharath Girish, Kamal Gupta, Saurabh Singh, Abhinav Shrivastava
We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off.
no code implementations • 15 Mar 2022 • Sharath Girish, Debadeepta Dey, Neel Joshi, Vibhav Vineet, Shital Shah, Caio Cesar Teodoro Mendes, Abhinav Shrivastava, Yale Song
We conduct a large-scale study with over 100 variants of ResNet and MobileNet architectures and evaluate them across 11 downstream scenarios in the SSL setting.
1 code implementation • ICCV 2021 • Sharath Girish, Saksham Suri, Saketh Rambhatla, Abhinav Shrivastava
Through extensive experiments, we show that our algorithm discovers unseen GANs with high accuracy and also generalizes to GANs trained on unseen real datasets.
1 code implementation • CVPR 2021 • Sharath Girish, Shishira R. Maiya, Kamal Gupta, Hao Chen, Larry Davis, Abhinav Shrivastava
The recently proposed Lottery Ticket Hypothesis (LTH) states that deep neural networks trained on large datasets contain smaller subnetworks that achieve on par performance as the dense networks.
no code implementations • 26 Dec 2018 • Anil Kumar Vadathya, Sharath Girish, Kaushik Mitra
Here, we present a unified learning framework that can reconstruct LF from a variety of multiplexing schemes with minimal number of coded images as input.