[Submitted on 14 Sep 2026]
Title:On The Robustness-Resolution Tradeoff In Temporal Quantization Of Event Streams
View a PDF of the paper titled On The Robustness-Resolution Tradeoff In Temporal Quantization Of Event Streams, by Sayeed Shafayet Chowdhury and 1 other authors
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Abstract:Event pipelines often discretize asynchronous timestamps before learning. This step looks harmless, but its stability depends directly on temporal resolution. We study this dependence at the representation level. We first show that hard temporal binning is discontinuous: an arbitrarily small timestamp shift near a boundary can move unit event mass between bins. We then define a class of nonnegative, mass-preserving, resolution-faithful continuous encoders and prove that every encoder in this class has global L1 sensitivity at least 2/Delta, where Delta denotes bin width. Linear two-bin interpolation attains this limit. Local support and first-moment preservation also make it unique. Experiments on SHD, N-MNIST, and DVS128 Gesture support the analysis. Across uniform timestamp budgets, linear interpolation lowers mean representation drift by 47-72% while keeping clean accuracy nearly unchanged. On DVS Gesture, it produces zero prediction flips across all tested budgets and three seeds. On SHD, measured drift follows 1/Delta with R^2 = 0.992.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.22295 [cs.CV]
(or arXiv:2609.22295v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.22295
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sayeed Shafayet Chowdhury [view email] [v1] Mon, 14 Sep 2026 11:51:03 UTC (78 KB)
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