# AutoSampling: Search for Effective Data Sampling Schedules

Source: [AutoSampling: Search for Effective Data Sampling Schedules](<https://www.sota2.com/research/paper/68fa6a041e209131cea25717>)

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

Data updated: 2025-10-25T23:13:37.567Z

Authors: Ming Sun, Haoxuan Dou, Baopu Li, Lei Cui, Junjie Yan, Wanli Ouyang

Published: 2021-05-28T09:39:41.000Z

## Abstract

Data sampling acts as a pivotal role in training deep learning models. However, an effective sampling schedule is difficult to learn due to the inherently high dimension of parameters in learning the sampling schedule. In this paper, we propose an AutoSampling method to automatically learn sampling schedules for model training, which consists of the multi-exploitation step aiming for optimal local sampling schedules and the exploration step for the ideal sampling distribution. More specifically, we achieve sampling schedule search with shortened exploitation cycle to provide enough supervision. In addition, we periodically estimate the sampling distribution from the learned sampling schedules and perturb it to search in the distribution space. The combination of two searches allows us to learn a robust sampling schedule. We apply our AutoSampling method to a variety of image classification tasks illustrating the effectiveness of the proposed method.

## 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/2105.13695>)

[PDF](<https://arxiv.org/pdf/2105.13695.pdf>)