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@ -18,16 +18,16 @@ Python Deep Outlier/Anomaly Detection (DeepOD)
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**DeepOD** is an open-source python framework for deep learning-based anomaly detection on multivariate/time-series data. DeepOD provides unified implementation of different detection models based on PyTorch.
**DeepOD** is an open-source Python framework for deep learning-based anomaly detection on multivariate/time-series data. DeepOD provides a unified implementation of different detection models based on PyTorch.
DeepOD includes 13 deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm) for now. More baseline algorithms will be included later.
🔭 *We are working on a new feature -- by simply setting a few parameters, different deep anomaly detection models can not only handle different data types.*
🔭 *We are working on a new feature -- by simply setting a few parameters, different deep anomaly detection models can handle different data types.*
- We have finished some attempts on partial models like Deep SVDD, DevNet, Deep SAD, PReNet and DIF. These models can use temporal networks like LSTM, GRU, TCN, Conv, Transformer to handle time series data.
- *Future work*: we also want to implement several network structure, so as to processing more data types like graphs and images by simply plugging in corresponding network architecture.
- We have finished some attempts on partial models like Deep SVDD, DevNet, Deep SAD, PReNet, and DIF. These models can use temporal networks like LSTM, GRU, TCN, Conv, and Transformer to handle time series data.
- *Future work*: we also want to implement several network structures, so as to process more data types like graphs and images by simply plugging in corresponding network architecture.
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