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...

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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.