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在深度神经网络中对图像分类的不精确性表示
IEEE transactions on neural networks and learning systems
|November 10, 2023
概括
本研究引入了深度学习中的不精确性表示 (RIDL) 来描述不确定性. 通过处理模两可的图像标签,RIDL提高了深度学习模型的准确性,并通过处理模两可的图像标签来表示不准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 深度学习的不确定性量化通常忽略不精确性表征.
- 一些任务从不准确的结果中获益,而不是昂贵的错误.
研究的目的:
- 调查深度学习 (RIDL) 技术中的不精确性表示.
- 在深度学习模型中描述和管理不确定性引起的不精确性.
主要方法:
- 使用神经网络和新的标签分配规则重建训练图像标签.
- 修改多标签图像以纠正标签错误,同时保留知识驱动的不精确性.
- 用重建的数据集重新训练深度网络模型并对测试图像进行分类.
主要成果:
- RIDL有效地描述了培训和测试数据集中的不准确性.
- 拟议的方法提高了分类准确性 (AC).
- 不准确的测试图像被分配到元类别中,代表了模两可.
结论:
- 在深度学习中,RIDL提供了一种新的方法来管理深度学习中的不确定性.
- 该技术提高了模型性能,并提供了不精确的有意义的表示.
- 这种方法对于理解模糊性至关重要的应用非常有价值.
相关概念视频
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Highly accurate measurements...
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Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Highly accurate measurements...

