Search Results for author:
Found 11 papers, 3 papers with code
Action-Affect Classification and Morphing using Multi-Task Representation Learning
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).
Deep Residual Learning in the JPEG Transform Domain
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.
Quantization Guided JPEG Artifact Correction
The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios.
Analyzing and Mitigating JPEG Compression Defects in Deep Learning
We show that there is a significant penalty on common performance metrics for high compression.
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer
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.
ReLaX: Retinal Layer Attribution for Guided Explanations of Automated Optical Coherence Tomography Classification
While previous works use pixel-level attribution methods for generating model explanations, our work uses a novel retinal layer attribution method for producing rich qualitative and quantitative model explanations.
A Frequency Perspective of Adversarial Robustness
Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent.
Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing.
The First Principles of Deep Learning and Compression
This allows the incredible advances in deep learning to be used for multimedia compression without threatening the ubiquity of the classical methods.
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling
This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video.
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions
We call the aggregate of these contribution maps the Implicit Neural Canvas and we use this concept to demonstrate that the INRs which we study learn to ''see'' the frames they represent in surprising ways.
