2 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.CV2023★ 2 cited
Knowing the Distance: Understanding the Gap Between Synthetic and Real Data For Face Parsing
Eli Friedman, Assaf Lehr, Alexey Gruzdev +4
The use of synthetic data for training computer vision algorithms has become increasingly popular due to its cost-effectiveness, scalability, and ability to provide accurate multi-…
cs.CV2022★ 1 cited
Hands-Up: Leveraging Synthetic Data for Hands-On-Wheel Detection
Paul Yudkin, Eli Friedman, Orly Zvitia +1
Over the past few years there has been major progress in the field of synthetic data generation using simulation based techniques. These methods use high-end graphics engines and p…
cs.LG2018
Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement Learning
Eli Friedman, Fred Fontaine
Many reinforcement-learning researchers treat the reward function as a part of the environment, meaning that the agent can only know the reward of a state if it encounters that sta…