半监督主动学习使用卷积自动编码器和对比学习
Hezi Roda1, Amir B Geva1,2
1Electrical and Computer Engineering, Ben-Gurion University, Be'er Sheva, Israel.
Frontiers in artificial intelligence
|June 14, 2024
概括
这项研究引入了一种用于图像分类的新型主动学习方法,利用未标记的数据来提高效率. 这种方法提高了模型的准确性,特别是当标记数据稀缺时.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 获取用于机器学习的标记数据通常是昂贵且耗时的.
- 积极学习旨在在预算限制范围内优化数据注释.
- 现有的积极学习方法往往忽视了未标记数据的潜力.
研究的目的:
- 为图像分类提出一种新的基于池的半监督主动学习方法.
- 加强在积极学习过程中对标记和未标记数据的利用.
- 用有限的标记样本提高图像分类的效率和准确性.
主要方法:
- 集群预先训练的卷积自编码器的隐藏空间.
- 采用集群对比损失来改进潜在空间集群,使用最小的标记数据.
- 在代过程中查询具有高不确定性的样本,以便由预言者进行注释.
主要成果:
- 提出的方法证明了有效性,特别是当注释样本的数量很少时.
- 在基准数据集上的实验验验证了该方法在图像分类中的有效性.
- 经验结果显示,在准确性方面有显著的改进.
结论:
- 开发的半监督主动学习方法有效地利用未标记的数据来提高图像分类性能.
- 这种方法为具有有限标记数据预算的场景提供了强大的解决方案.
- 该研究强调了将未标记的数据整合到计算机视觉任务的积极学习策略中的价值.
更多相关视频
相关概念视频
Associative Learning
335
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Classical conditioning, also known...
335
Force Classification
1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Deconvolution
154
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
154
Convolution Properties II
180
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
180
Active Filters
818
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
818
Difference from Background: Limit of Detection
6.3K
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...
6.3K


