1 citations · 2 across the 6 of their papers we have counts for
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FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
Alon Shoshan, Nadav Bhonker, Emanuel Ben Baruch +5
Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive persona…
Asymmetric Image Retrieval with Cross Model Compatible Ensembles
Ori Linial, Alon Shoshan, Nadav Bhonker +4
The asymmetrical retrieval setting is a well suited solution for resource constrained applications such as face recognition and image retrieval. In this setting, a large model is u…
Synthetic Data for Model Selection
Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky +2
Recent breakthroughs in synthetic data generation approaches made it possible to produce highly photorealistic images which are hardly distinguishable from real ones. Furthermore,…
GAN-Control: Explicitly Controllable GANs
Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky +1
We present a framework for training GANs with explicit control over generated images. We are able to control the generated image by settings exact attributes such as age, pose, exp…
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding
Igor Kviatkovsky, Nadav Bhonker, Gerard Medioni
We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario. These images benefit from the advantages of synthetic data: bei…