In addition, our method can be readily applied for controlling other degrees of freedom of light in the SPDC process, such as the spectral and temporal properties, and may even be used in condensed-matter systems having a similar interaction Hamiltonian.
In this work, we introduce image denoisers derived as the gradients of smooth scalar-valued deep neural networks, acting as potentials.
Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurements.
We introduce a systematic approach for designing 3D nonlinear photonic crystals and pump beams for generating desired quantum correlations between structured photon-pairs.
A long-standing challenge in multiple-particle-tracking is the accurate and precise 3D localization of individual particles at close proximity.
Generating training examples for supervised tasks is a long sought after goal in AI.
Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.
Functional muscle imaging is essential for diagnostics of a multitude of musculoskeletal afflictions such as degenerative muscle diseases, muscle injuries, muscle atrophy, and neurological related issues such as spasticity.
We present such a technique for localization with limited annotation, in which the number of images with bounding boxes can be a small fraction of the total dataset (e. g. less than 1%); all other images only possess a whole image label and no bounding box.
Localization microscopy is an imaging technique in which the positions of individual nanoscale point emitters (e. g. fluorescent molecules) are determined at high precision from their images.
Instead of feeding the network with synthetic data, we solely use real-world outdoor images and tune the network's parameters by directly minimizing the DCP.
Ranked #9 on Image Dehazing on SOTS Outdoor
Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies.
The success of deep learning has been due, in no small part, to the availability of large annotated datasets.
Ranked #10 on Image Dehazing on SOTS Outdoor
We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset.
We present ASIST, a technique for transforming point clouds by replacing objects with their semantically equivalent counterparts.
The classifier architecture is designed to optimize both classification speed and accuracy when a large training set is available.