Alibi Detect
  • Alibi Detect
  • Getting Started
  • Algorithm Overview
  • Saving and Loading
  • Detector Configuration Files
  • Roadmap
  • Outlier Detection
    • Methods
      • Mahalanobis Distance
      • Isolation Forest
      • Variational Auto-Encoder
      • Auto-Encoder
      • Variational Auto-Encoding Gaussian Mixture Model
      • Auto-Encoding Gaussian Mixture Model
      • Likelihood Ratios for Outlier Detection
      • Prophet Detector
      • Spectral Residual
      • Sequence-to-Sequence (Seq2Seq)
    • Examples
      • AE outlier detection on CIFAR10
      • AEGMM and VAEGMM outlier detection on KDD Cup ‘99 dataset
      • Isolation Forest outlier detection on KDD Cup ‘99 dataset
      • Likelihood Ratio Outlier Detection on Genomic Sequences
      • Likelihood Ratio Outlier Detection with PixelCNN++
      • Mahalanobis outlier detection on KDD Cup ‘99 dataset
      • Time-series outlier detection using Prophet on weather data
      • Seq2Seq time series outlier detection on ECG data
      • Time series outlier detection with Seq2Seq models on synthetic data
      • Time series outlier detection with Spectral Residuals on synthetic data
      • VAE outlier detection for income prediction
      • VAE outlier detection on CIFAR10
      • VAE outlier detection on KDD Cup ‘99 dataset
  • Drift Detection
    • Methods
      • Offline
        • Chi-Squared
        • Kolmogorov-Smirnov
        • Cramér-von Mises
        • Fisher’s Exact Test
        • Maximum Mean Discrepancy
        • Least-Squares Density Difference
        • Learned Kernel
        • Classifier
        • Spot-the-diff
        • Model Uncertainty
        • Mixed-type tabular data
        • Context-Aware Maximum Mean Discrepancy
      • Online
        • Online Maximum Mean Discrepancy
        • Online Least-Squares Density Difference
        • Online Cramér-von Mises
        • Online Fisher’s Exact Test
    • Examples
      • Categorical and mixed type data drift detection on income prediction
      • Learned drift detectors on Adult Census
      • Learned drift detectors on CIFAR-10
      • Context-aware drift detection on news articles
      • Context-aware drift detection on ECGs
      • Model Distillation drift detector on CIFAR-10
      • Kolmogorov-Smirnov data drift detector on CIFAR-10
      • Maximum Mean Discrepancy drift detector on CIFAR-10
      • Scaling up drift detection with KeOps
      • Model uncertainty based drift detection on CIFAR-10 and Wine-Quality datasets
      • Drift detection on molecular graphs
      • Online drift detection for Camelyon17 medical imaging dataset
      • Online Drift Detection on the Wine Quality Dataset
      • Interpretable drift detection with the spot-the-diff detector on MNIST and Wine-Quality datasets
      • Supervised drift detection on the penguins dataset
      • Drift detection on Amazon reviews
      • Text drift detection on IMDB movie reviews
  • Adversarial Detection
    • Methods
      • Adversarial Auto-Encoder
      • Model Distillation
    • Examples
      • Adversarial AE detection and correction on CIFAR-10
  • Deployment
  • Datasets
  • Models
  • Bibliography
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Models

Models and/or building blocks that can be useful outside of outlier, adversarial or drift detection can be found under alibi_detect.models. Main implementations:

  • PixelCNN++: from alibi_detect.models.tensorflow import PixelCNN

  • Variational Autoencoder: from alibi_detect.models.tensorflow import VAE

  • Sequence-to-sequence model: from alibi_detect.models.tensorflow import Seq2Seq

  • ResNet: from alibi_detect.models.tensorflow import resnet

Pre-trained TensorFlow ResNet-20/32/44 models on CIFAR-10 can be found on our Google Cloud Bucket and can be fetched as follows:

from alibi_detect.utils.fetching import fetch_tf_model

model = fetch_tf_model('cifar10', 'resnet32')
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Last updated 5 months ago

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