Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requires training a neural network for each representation and selecting the best one based on the validation score, which is very time-consuming. In this work, we eliminate this bottleneck by selecting the best representation based on the Gromov-Wasserstein Discrepancy (GWD) between the raw events and their representation. It is approximately 200 times faster to compute than training a neural network and preserves the task performance ranking of event representations across multiple representations, network backbones, and datasets. This means that finding a representation with a high task score is equivalent to finding a representation with a low GWD. We use this insight to, for the first time, perform a hyperparameter search on a large family of event representations, revealing new and powerful representations that exceed the state-of-the-art. On object detection, our optimized representation outperforms existing representations by 1.9% mAP on the 1 Mpx dataset and 8.6% mAP on the Gen1 dataset and even outperforms the state-of-the-art by 1.8% mAP on Gen1 and state-of-the-art feed-forward methods by 6.0% mAP on the 1 Mpx dataset. This work opens a new unexplored field of explicit representation optimization for event-based learning methods.

翻译：当今，处理事件数据的先进深度神经网络首先将事件转换为稠密的网格状输入表示，再使用现成网络进行处理。然而，传统上为任务选择合适表示需针对每种表示训练神经网络，并根据验证分数择优，这一过程极为耗时。本文通过计算原始事件与其表示之间的Gromov-Wasserstein差异（GWD）来选择最优表示，从而消除了这一瓶颈。该方法的计算速度比训练神经网络快约200倍，且在多种表示、网络骨架和数据集上均能保持事件表示的任务性能排名。这意味着，获得高任务分数的表示等价于找到低GWD的表示。基于这一发现，我们首次对大规模事件表示族进行超参数搜索，揭示了超越现有技术的新颖且强大的表示。在目标检测任务中，我们的优化表示在1 Mpx数据集上超出已有表示1.9% mAP，在Gen1数据集上超出8.6% mAP，甚至在Gen1上以1.8% mAP超越现有技术，在1 Mpx数据集上以6.0% mAP超越前馈方法。这项工作为事件驱动学习方法开辟了一个此前未被探索的显式表示优化新领域。