适应增强:一种无调和自适应的方法来增强数据增强
概括
通过强化学习,AdaAugment可以动态调整数据增强的大小. 这种适应性方法通过将增强数据与培训进展相协调,改善深度模型通用化,防止不足和过度装配.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 数据增强 (DA) 对于增强深度模型通用化至关重要.
- 当前的DA方法经常使用固定或随机的增强大小,导致与模型训练状态的潜在错位.
- 这种错位可能会增加装配不足和装配过多的风险.
研究的目的:
- 介绍AdaAugment,一个新的,无调节的自适应数据增强方法.
- 通过实时网络反来动态调整单个训练样本的增强大小.
- 通过调整增强数据与模型培训进展来减轻装配不足和过度装配.
主要方法:
- AdaAugment采用双重模型架构:一个政策网络和一个目标网络.
- 政策网络通过强化学习自适应地调整增强大小.
- 政策和目标网络共同优化,目标网络在自适应增强样本上进行培训.
主要成果:
- AdaAugment的性能始终优于最先进的数据增强方法.
- 该方法在基准数据集和深度架构中显示出卓越的有效性.
- 在训练期间,AdaAugment保持了显著的计算效率.
结论:
- AdaAugment为适应性数据增强提供了有效和高效的解决方案.
- 提出的方法成功地解决了固定/随机增强策略的局限性.
- 通过智能地将增强调整到训练动态,AdaAugment增强了深度模型概括.
相关概念视频
Improving Translational Accuracy
2.7K
2.7K
Mutation, Gene Flow, and Genetic Drift
59.5K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
59.5K
Genetic Drift
40.7K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
40.7K
Randomized Experiments
7.2K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
7.2K
Data Collection by Experiments
25.2K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
An example of the experimental method is a public...
25.2K
Random Sampling Method
12.4K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
12.4K
