从全球到本地:多规模的分布外检测检测
概括
本研究介绍了多尺度OOD检测 (MODE),这是一个用于分布外 (OOD) 检测的新框架. 通过整合全球和本地图像细节,MODE提高了检测准确性,优于以前的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 分布外 (OOD) 检测可以识别训练期间未见的数据.
- 基于距离的OOD检测使用全局图像表示,由于背景杂乱和类内变化,这些表示可能不足于最佳.
- 现有的方法难以捕捉局部细节,这对于强大的OOD检测至关重要.
研究的目的:
- 提出一个新的框架,多尺度OOD检测 (MODE),以改进OOD检测.
- 利用全球和本地图像信息来更准确地识别未知的数据.
- 解决现有方法在利用局部表示用于OOD检测方面的局限性.
主要方法:
- MODE框架将全球视觉信息与当地地区的细节相结合.
- 基于注意力的本地宣传 (ALPA) 目标鼓励在培训期间在当地进行歧视性代表.
- 跨尺度决策 (CSD) 函数使用多尺度表示来在测试时检测OOD.
主要成果:
- MODE有效地利用全球和本地图像功能进行OOD检测.
- ALPA的目标是成功地使相关的当地地区保持一致并突出显示,从而提高了代表性的质量.
- 在基准数据集上,MODE表现出与最先进的方法相比更优异的性能.
结论:
- 通过结合多层次信息,MODE在OOD检测方面取得了重大进展.
- 拟议的ALPA和CSD组件增强了该模型区分分发行数据和分销数据的能力.
- MODE 提供了一个灵活而有效的解决方案来应对现实世界的 OOD 检测挑战.
相关概念视频
Quantifying and Rejecting Outliers: The Grubbs Test
1.6K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Detection of Gross Error: The Q Test
6.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.1K
Steps in Outbreak Investigation
135
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
135
Difference from Background: Limit of Detection
6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
6.4K
What Are Outliers?
3.9K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
3.9K
Outliers and Influential Points
4.1K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.1K


