域名通用化与相关的风格不确定性
Zheyuan Zhang1, Bin Wang1, Debesh Jha1
1Machine & Hybrid Intelligence Lab, Northwestern University, USA.
概括
本研究介绍了相关的风格不确定性 (CSU),这是一种新的域泛化方法,通过在风格增强过程中保留特征相关性来增强深度学习模型. CSU显著提高了各种计算机视觉和医疗成像任务的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 域泛化 (DG) 旨在通过学习域不变特征来创建强大的深度学习模型.
- 样式增强是一种强大的DG技术,使用实例特定的特征统计数据合成新域.
- 现有的风格增强方法往往忽视特征道的相互依赖性或使用有限的线性插值.
研究的目的:
- 引入一种新的风格增强方法,即相关风格不确定性 (CSU),以解决当前域泛化方法的局限性.
- 在风格增强过程中,保留功能通道之间的关联信息.
- 提高深度学习模型在不同领域的稳定性和性能.
主要方法:
- 开发了相关的风格不确定性 (CSU),这是一个新的增强方法,用于域泛化.
- CSU克服了在风格统计空间中的线性插值的局限性.
- 保存不同功能通道之间的关键相关信息.
主要成果:
- 在多个跨领域的任务中,CSU在最先进的技术上取得了显著的改进.
- 在计算机视觉数据集 (PACS,Office-Home,Duke-Market1501) 和医学成像 (Camelyon17) 上进行了实验.
- 该方法在分类和实例检索任务中显示了增强的性能.
结论:
- 相关的风格不确定性 (CSU) 为域泛化提供了一种优越的风格增强方法.
- 该方法通过保留特征相关性来提高模型的稳定性.
- CSU代表了在创建更可泛化的深度学习模型方面取得的重大进展.
相关概念视频
Propagation of Uncertainty from Systematic Error
502
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
502
Propagation of Uncertainty from Random Error
664
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
664
Uncertainty: Overview
535
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
535
Generalization, Discrimination, and Extinction
519
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
519
Random Error
865
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
865
Uncertainty: Confidence Intervals
3.5K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
3.5K


