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Found 9 papers, 1 papers with code
BiPer: Binary Neural Networks using a Periodic Function
In contrast to current BNN approaches, we propose to employ a binary periodic (BiPer) function during binarization.
Ranked #1 on
Classification with Binary Neural Network
on ImageNet
(Top-1 metric)
Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems
Compressive imaging using coded apertures (CA) is a powerful technique that can be used to recover depth, light fields, hyperspectral images and other quantities from a single snapshot.
Joint Radar-Communications Processing from a Dual-Blind Deconvolution Perspective
When the radar receiver is not collocated with the transmitter, such as in passive or multistatic radars, the transmitted signal is also unknown apart from the target parameters.
Deep Optical Coding Design in Computational Imaging
The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task.
Fast Disparity Estimation from a Single Compressed Light Field Measurement
Specifically, we propose to jointly optimize an optical architecture for acquiring a single coded light field snapshot and a convolutional neural network (CNN) for estimating the disparity maps.
Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications
In this dual-blind deconvolution (DBD) problem, the receiver admits a multi-carrier wireless communications signal that is overlaid with the radar signal reflected off multiple targets.
Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging.
Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization
In these passive listening outposts, the signals and channels of both radar and communications are unknown to the receiver.
Depth Estimation from a Single Optical Encoded Image using a Learned Colored-Coded Aperture
Depth estimation from a single image of a conventional camera is a challenging task since depth cues are lost during the acquisition process.
