数据集蒸:一个全面的审查
IEEE transactions on pattern analysis and machine intelligence
|October 10, 2023
概括
数据集蒸 (DD) 创建更小的合成数据集用于训练深度学习模型,减少存储和隐私问题. 这篇综述涵盖了DD方法,挑战和未来的研究方向.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 深度学习模型需要大量的数据集,导致存储,传输和隐私问题.
- 传统的培训方法面临着数据量,计算成本和数据安全方面的挑战.
研究的目的:
- 提供数据集蒸 (DD) 和其应用的全面审查.
- 系统地对现有的DD方法进行分类和分析.
- 确定DD的当前挑战和未来的研究途径.
主要方法:
- 数据集蒸任务的正式引入.
- 关于DD的一般算法框架的建议.
- 现有DD技术及其理论基础的分类和讨论.
主要成果:
- 一个结构化的概述数据集蒸的当前景观.
- 通过实证研究确定关键挑战.
- 洞察不同DD方法之间的理论联系.
结论:
- 数据集蒸提供了一个有希望的解决方案,以减轻深度学习中的数据负担.
- 需要进一步的研究来应对现有的挑战,并探索DD的新前沿.
- 该审查为进入数据集蒸领域的研究人员提供了宝贵的资源.
更多相关视频
03:31Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
568
12:26Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
Published on: October 11, 2016
13.4K
相关概念视频
Distillation: Vapor–Liquid Equilibria
2.8K
Distillation is a separation technique that takes advantage of the boiling point properties of disparate elements in a mixture. To perform distillation, we begin by heating a miscible mixture of two liquids with a significant difference in boiling points (at least 20°C). As the solution heats up and reaches the bubble point of the more volatile component, some molecules of the more volatile component transition into the gas phase and travel upward into the condenser, which is a glass tube...
2.8K
Distribution Reliability and Automation
110
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
110
Statistical Analysis: Overview
6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K
Sampling Distribution
12.9K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.9K
Extraction: Partition and Distribution Coefficients
2.5K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
For extracting a solute from an aqueous phase into an...
2.5K
Distributions to Estimate Population Parameter
4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
