no code implementations • 4 Apr 2022 • Max Ehrlich
This allows the incredible advances in deep learning to be used for multimedia compression without threatening the ubiquity of the classical methods.
no code implementations • 31 Jan 2022 • Max Ehrlich, Jon Barker, Namitha Padmanabhan, Larry Davis, Andrew Tao, Bryan Catanzaro, Abhinav Shrivastava
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing.
no code implementations • 26 Oct 2021 • Shishira R Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava
Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent.
no code implementations • 3 Sep 2021 • Evan Wen, Max Ehrlich
In combination with an OCT segmentation model, this allows us to produce quantitative breakdowns of the specific retinal layers the model focused on for later review by an expert.
no code implementations • 16 May 2021 • Arthita Ghosh, Max Ehrlich, Larry Davis, Rama Chellappa
Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images.
no code implementations • 17 Nov 2020 • Max Ehrlich, Larry Davis, Ser-Nam Lim, Abhinav Shrivastava
We show that there is a significant penalty on common performance metrics for high compression.
1 code implementation • ECCV 2020 • Max Ehrlich, Larry Davis, Ser-Nam Lim, Abhinav Shrivastava
The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios.
1 code implementation • ICCV 2019 • Max Ehrlich, Larry Davis
We introduce a general method of performing Residual Network inference and learning in the JPEG transform domain that allows the network to consume compressed images as input.
no code implementations • 21 Mar 2016 • Timothy J. Shields, Mohamed R. Amer, Max Ehrlich, Amir Tamrakar
We propose a new model that enhances the CRBM model with a factored multi-task component to become Multi-Task Conditional Restricted Boltzmann Machines (MTCRBMs).