Scaling Motion Forecasting Models with Ensemble Distillation for Efficient Deployment on Autonomous Robots
核心概念
Ensemble models can significantly improve the accuracy of motion forecasting systems, but their high computational cost makes them impractical for deployment on autonomous robots. This work develops a framework to distill large ensembles into smaller student models that retain high performance at a fraction of the compute cost, enabling efficient deployment on resource-constrained robotic platforms.
要約
The paper presents a method for scaling the performance of motion forecasting models through ensemble techniques and distillation.
The key insights are:
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Ensembling multiple independently trained motion forecasting models can significantly improve performance on metrics like minADE and soft-mAP, but at the cost of high computational requirements.
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To address this, the authors develop a generalized framework for distilling the knowledge from an ensemble of teacher models into a smaller student model. This allows retaining the high accuracy of the ensemble while reducing the compute cost for deployment.
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The distillation process is tailored to handle the multi-modal output distribution of motion forecasting models, using techniques like non-maximal suppression to aggregate the ensemble outputs and a custom distillation loss to train the student.
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Experiments on the Waymo Open Motion Dataset and Argoverse 2 show that the distilled student models achieve high performance, outperforming single baseline models while requiring significantly fewer FLOPs for inference.
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The ensemble models themselves also achieve state-of-the-art results on the benchmarks, ranking highly on the leaderboards.
The proposed ensemble distillation framework enables scaling motion forecasting performance for autonomous robots by combining the strengths of ensemble models and knowledge distillation.
Scaling Motion Forecasting Models with Ensemble Distillation
統計
The Waymo Open Motion Dataset contains over 570 hours of driving data including maps, traffic light states, and agent motion data sampled at 10Hz.
The Argoverse 2 Motion Forecasting dataset consists of 250,000 scenarios with agent motion data sampled at 10Hz.
引用
"Motion forecasting has become an increasingly critical component of autonomous robotic systems. Onboard compute budgets typically limit the accuracy of real-time systems."
"We propose a method of using distillation to bring the cost back down to within the onboard compute budget while retaining high performance."
深掘り質問
How can the ensemble distillation framework be extended to handle other types of structured prediction tasks beyond motion forecasting?
The ensemble distillation framework can be extended to handle other structured prediction tasks by adapting the model architecture and loss functions to suit the specific task requirements. For tasks like natural language processing or image segmentation, where the output is a sequence or a structured representation, the ensemble models can be designed to output distributions over the possible outcomes. The distillation process would then involve training the student model to mimic the distribution of the ensemble models. Additionally, for tasks with different input modalities or data types, the ensemble can consist of models specialized in processing each type of input, and the distillation process can focus on integrating these diverse outputs into a coherent prediction.
What are the limitations of the current distillation approach, and how could it be further improved to better preserve the diversity of the ensemble outputs?
One limitation of the current distillation approach is that it may struggle to preserve the diversity of the ensemble outputs, especially when the ensemble models have significantly different predictions. To address this limitation, the distillation process could incorporate techniques to encourage diversity in the student model's predictions. For example, introducing regularization terms that penalize the student model for producing similar outputs for different inputs can help maintain the diversity present in the ensemble. Additionally, exploring more sophisticated distillation loss functions that explicitly consider the diversity of predictions across the ensemble models can further improve the preservation of diversity in the distilled student model.
Can the ensemble and distillation techniques be combined with other model scaling approaches like conditional computation or dynamic model selection to further improve efficiency?
Yes, combining ensemble and distillation techniques with other model scaling approaches like conditional computation or dynamic model selection can lead to further improvements in efficiency. Conditional computation techniques, such as using attention mechanisms to focus on relevant parts of the input, can be integrated into the ensemble models to enhance their predictive capabilities. Dynamic model selection methods can be employed to choose the most suitable model from the ensemble for a given input, reducing computational costs while maintaining high performance. By leveraging these additional scaling approaches in conjunction with ensembling and distillation, the overall efficiency and effectiveness of the predictive models can be significantly enhanced.
