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：当前，处理事件的最先进深度神经网络首先将事件转换为密集的网格状输入表示，再使用现成的网络进行处理。然而，为任务选择合适的表示传统上需要为每种表示训练一个神经网络，并基于验证分数选择最佳表示，这一过程极为耗时。在本工作中，我们通过基于原始事件与其表示之间的格罗莫夫-瓦瑟斯坦差异（GWD）来选择最佳表示，从而消除了这一瓶颈。其计算速度比训练神经网络快约200倍，且在多种表示、网络骨干和数据集上保持了事件表示的任务性能排名。这意味着，找到具有高任务分数的表示等价于找到低GWD值的表示。基于这一洞见，我们首次对大规模事件表示族进行了超参数搜索，发现了超越现有最优水平的强大新表示。在目标检测任务中，我们的优化表示在1 Mpx数据集上以1.9% mAP的优势超越现有表示，在Gen1数据集上以8.6% mAP的优势领先，甚至在Gen1上以1.8% mAP超越现有最优方法，在1 Mpx数据集上以6.0% mAP超越当前最先进的前馈方法。本工作为基于事件的学习方法开辟了一个未探索的显式表示优化新领域。