验证
深度学习验证是一套评估深度神经网络属性的方法。例如,您可以验证网络的稳健性属性、计算网络输出边界、查找对抗示例以及检测分布外数据,并验证与行业标准的合规性。
Deep Learning Toolbox Verification Library 支持包能够测试深度学习网络的稳健性属性。
使用
verifyNetworkRobustness
函数验证网络对对抗示例的稳健性。当在指定的输入下界和上界之间扰动输入时,如果网络的预测类不改变,则表明网络针对对抗输入具有稳健性。对于一组输入边界,该函数检查网络对于那些输入边界之间的对抗示例是否具有稳健性,并返回verified
、violated
或unproven
。当输入介于指定的下界和上界之间时,使用
estimateNetworkOutputBounds
函数来估计网络返回的输出值的范围。使用此函数估计网络预测对输入扰动的敏感度。使用
networkDistributionDiscriminator
函数创建一个分布判别器,该判别器将数据分为分布内和分布外。
函数
estimateNetworkOutputBounds | Estimate output bounds of deep learning network (自 R2022b 起) |
verifyNetworkRobustness | Verify adversarial robustness of deep learning network (自 R2022b 起) |
networkDistributionDiscriminator | Deep learning distribution discriminator (自 R2023a 起) |
isInNetworkDistribution | Determine whether data is within the distribution of the network (自 R2023a 起) |
distributionScores | Distribution confidence scores (自 R2023a 起) |
drise | Explain object detection network predictions using D-RISE (自 R2024a 起) |
对象
BaselineDistributionDiscriminator | Baseline distribution discriminator (自 R2023a 起) |
EnergyDistributionDiscriminator | Energy distribution discriminator (自 R2023a 起) |
ODINDistributionDiscriminator | ODIN distribution discriminator (自 R2023a 起) |
HBOSDistributionDiscriminator | HBOS distribution discriminator (自 R2023a 起) |
主题
- Verification of Neural Networks
Learn about verification of neural networks using Deep Learning Toolbox™ Verification Library.
- Verify Robustness of Deep Learning Neural Network
This example shows how to verify the adversarial robustness of a deep learning neural network.
- Verify Robustness of ONNX Network
This example shows how to verify the adversarial robustness of an imported ONNX™ deep neural network. (自 R2024a 起)
- Deep Learning Visualization Methods
Learn about and compare deep learning visualization methods.
- Out-of-Distribution Detection for Deep Neural Networks
This example shows how to detect out-of-distribution (OOD) data in deep neural networks.
- Verify an Airborne Deep Learning System
This example shows how to verify a deep learning system for airborne applications and is based on the work in [5,6,7], which includes the development and verification activities required by DO-178C [1], ARP4754A [2], and prospective EASA and FAA guidelines [3,4]. (自 R2023b 起)