Using Rotation, Translation, and Cropping to Boost Generalization in Deep Reinforcement Learning Models | Synced
In a new paper, researchers from the New York University and Modl.ai, a company applying machine learning to game developing, suggest that simple spacial processing methods such as rotation, transl...
Source: Synced | AI Technology & Industry Review
In a new paper, researchers from the New York University and Modl.ai, a company applying machine learning to game developing, suggest that simple spacial processing methods such as rotation, translation and cropping could help increase model generality.