Knowing the Distance: Understanding the Gap Between Synthetic and Real Data For Face Parsing

no code implementations • 27 Mar 2023 • Eli Friedman, Assaf Lehr, Alexey Gruzdev, Vladimir Loginov, Max Kogan, Moran Rubin, Orly Zvitia

Our study highlights the importance of content diversity in synthetic datasets and challenges the notion that the photorealism gap is the most critical factor affecting the performance of computer vision models trained on synthetic data.

Face Parsing

TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set

no code implementations • 17 Dec 2018 • Moran Rubin, Omer Stein, Nir A. Turko, Yoav Nygate, Darina Roitshtain, Lidor Karako, Itay Barnea, Raja Giryes, Natan T. Shaked

After this preliminary training, and after transforming the last layer of the network with new ones, we have designed an automatic classifier for the correct cell type (healthy/primary cancer/metastatic cancer) with 90-99% accuracy, although small training sets of down to several images have been used.

General Classification Generative Adversarial Network +3