# Lost in Transmission: On the Impact of Networking Corruptions on Video Machine Learning Models

Source: [Lost in Transmission: On the Impact of Networking Corruptions on Video Machine Learning Models](<https://www.sota2.com/research/paper/68fa6cbe1e209131cea35b52>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:17:38.470Z

Authors: Trenton Chang, Daniel Y. Fu

Published: 2022-06-10T17:50:50.000Z

## Abstract

We study how networking corruptions--data corruptions caused by networking errors--affect video machine learning (ML) models. We discover apparent networking corruptions in Kinetics-400, a benchmark video ML dataset. In a simulation study, we investigate (1) what artifacts networking corruptions cause, (2) how such artifacts affect ML models, and (3) whether standard robustness methods can mitigate their negative effects. We find that networking corruptions cause visual and temporal artifacts (i.e., smeared colors or frame drops). These networking corruptions degrade performance on a variety of video ML tasks, but effects vary by task and dataset, depending on how much temporal context the tasks require. Lastly, we evaluate data augmentation--a standard defense for data corruptions--but find that it does not recover performance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.05252>)

[PDF](<https://arxiv.org/pdf/2206.05252.pdf>)