Tree-managed network ensembles for video prediction

This paper presents an innovative approach that leverages a tree structure to effectively manage a large ensemble of neural networks for tackling complex video prediction tasks. Our proposed method introduces a novel technique for partitioning the function domain into simpler subsets, enabling piecewise learning by the ensemble. Seamlessly accessed by an accompanying tree structure with a time complexity of O(log(N)), this ensemble-tree framework progressively expands while training examples become more complex. The tree construction process incorporates a specialized algorithm that utilizes localized comparison functions, learned at each decision node. To evaluate the effectiveness of our method, we conducted experiments in two challenging scenarios: action-conditional video prediction in a 3D video game environment and error detection in real-world 3D printing scenarios. Our approach consistently outperformed existing methods by a significant margin across various experiments. Additionally, we introduce a new evaluation methodology for long-term video prediction tasks, which demonstrates improved alignment with qualitative observations. The results highlight the efficacy and superiority of our ensemble-tree approach in addressing complex video prediction challenges.