This allows the incredible advances in deep learning to be used for multimedia compression without threatening the ubiquity of the classical methods.
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing.
Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent.
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.
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.
The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios.
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.
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).