├── .gitignore ├── CIFAR-10 ├── Benchmark_CIFAR10_Anomaly=1_Cosine.ipynb ├── Benchmark_CIFAR10_Anomaly=1_Euclidean.ipynb ├── Benchmark_CIFAR10_Anomaly=5_Cosine.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine_N=100_Again.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine_N=100_Pred=Min10Mean.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine_N=100_Pred=Min20Mean.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine_N=100_Pred=Min30Mean.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine_N=100_Pred=Min5Mean.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine_N=200.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine_N=300.ipynb ├── Benchmark_CIFAR10_Anomaly=8_Cosine_N=50.ipynb └── Benchmark_CIFAR10_Anomaly=8_Euclidean.ipynb ├── LICENSE ├── MNIST ├── Benchmark_MNIST_Anomaly=5_Cosine.ipynb ├── Benchmark_MNIST_Anomaly=5_Euclidean.ipynb └── Benchmark_MNIST_Anomaly=7_Cosine.ipynb ├── MVTecAD ├── Capsule_91%.ipynb ├── Consideration_Learned_Weight_As_Center.ipynb ├── Grid_99%.ipynb ├── MVTecAD_BinaryClf.ipynb ├── MVTecAD_MultiClsClf.ipynb ├── MVTecAD_SelfSupervised.ipynb ├── MVTecAD_SelfSupervised_BlobAware.ipynb ├── Pill_upto93%.ipynb ├── Screw_80to90%.ipynb └── Transistor_upto95%.ipynb ├── README.md ├── anomaly_twin_imagelist.py ├── generate_figures.ipynb ├── metric_nets.py ├── mvtecad_test.py ├── util_visualize.py └── utils.py /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/daisukelab/metric_learning/HEAD/.gitignore -------------------------------------------------------------------------------- /CIFAR-10/Benchmark_CIFAR10_Anomaly=1_Cosine.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/daisukelab/metric_learning/HEAD/CIFAR-10/Benchmark_CIFAR10_Anomaly=1_Cosine.ipynb -------------------------------------------------------------------------------- /CIFAR-10/Benchmark_CIFAR10_Anomaly=1_Euclidean.ipynb: 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