refine Industry-strength AD naming (#601)

Since AD is a task (just like NLP, CV, RecSys) rather than a specific attribute of machine learning models/systems (such as robustness, explainability, performance). This module should be named a bit for consistency with other industry-strength sections.
EthicalML · Sep 11, 2024 · bdf68c0 · bdf68c0
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commit bdf68c0
Showing 1 changed file with 18 additions and 18 deletions.
diff --git a/README.md b/README.md
@@ -13,11 +13,11 @@ This repository contains a curated list of awesome open source libraries that wi
 
 | | | |
 |-|-|-|
-| [⚔ Adversarial Robustness](#adversarial-robustness) | [🔴 Anomaly Detection](#anomaly-detection) | [🤖 AutoML](#automl) |
-| [🗺️ Computation Load Distribution](#computation-load-distribution) | [🏷️ Data Labelling & Synthesis](#data-labelling-and-synthesis) | [🧵 Data Pipeline](#data-pipeline) |
-| [📓 Data Science Notebook](#ds-notebook) | [💾 Data Storage Optimisation](#data-storage-optimisation) | [💸 Data Stream Processing](#data-stream-processing) |
-| [💪 Deployment & Serving](#deployment-and-serving) | [📈 Evaluation & Observability](#evaluation-and-observability) | [🔍 Explainability & Interpretability](#explainability-and-interpretability) |
-| [🎁 Feature Store](#feature-store) | [👁️ Industry-strength Computer Vision](#industry-strength-cv) | [🔠 Industry-strength Natural Language Processing](#industry-strength-nlp) |
+| [⚔ Adversarial Robustness](#adversarial-robustness) | [🤖 AutoML](#automl) | [🗺️ Computation Load Distribution](#computation-load-distribution) |
+| [🏷️ Data Labelling & Synthesis](#data-labelling-and-synthesis) | [🧵 Data Pipeline](#data-pipeline) | [📓 Data Science Notebook](#ds-notebook) |
+| [💾 Data Storage Optimisation](#data-storage-optimisation) | [💸 Data Stream Processing](#data-stream-processing) | [💪 Deployment & Serving](#deployment-and-serving) |
+| [📈 Evaluation & Observability](#evaluation-and-observability) | [🔍 Explainability & Interpretability](#explainability-and-interpretability) | [🎁 Feature Store](#feature-store) |
+| [🔴 Industry-strength Anomaly Detection](#industry-strength-ad) | [👁️ Industry-strength Computer Vision](#industry-strength-cv) | [🔠 Industry-strength Natural Language Processing](#industry-strength-nlp) |
 | [🙌 Industry-strength Recommender System](#industry-strength-recsys) | [🍕 Industry-strength Reinforcement Learning](#industry-strength-rl) | [📊 Industry-strength Visualisation](#industry-strength-visualisation) |
 | [📅 Metadata Management](#metadata-management) | [📜 Model, Data & Experiment Tracking](#model-data-and-experiment-tracking) | [🔩 Model Compilation, Compression & Optimization](#model-compilation-compression-and-optimization) |
 | [🔥 Neural Search](#neural-search) | [🧮 Optimized Computation](#optimized-computation) | [🔏 Privacy & Security](#privacy-security) |
@@ -98,19 +98,6 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [OpenAttack](https://github.com/thunlp/OpenAttack) ![](https://img.shields.io/github/stars/thunlp/OpenAttack.svg?style=social) - OpenAttack is a Python-based textual adversarial attack toolkit, which handles the whole process of textual adversarial attacking, including preprocessing text, accessing the victim model, generating adversarial examples and evaluation.
 
 
-## Anomaly Detection
-* [adtk](https://github.com/arundo/adtk) ![](https://img.shields.io/github/stars/arundo/adtk.svg?style=social)  - A Python toolkit for rule-based/unsupervised anomaly detection in time series.
-* [Alibi Detect](https://github.com/SeldonIO/alibi-detect) ![](https://img.shields.io/github/stars/SeldonIO/alibi-detect.svg?style=social) - alibi-detect is a Python package focused on outlier, adversarial and concept drift detection.
-* [Darts](https://github.com/unit8co/darts) ![](https://img.shields.io/github/stars/unit8co/darts.svg?style=social) - Darts is a library for user-friendly forecasting and anomaly detection on time series.
-* [Deequ](https://github.com/awslabs/deequ) ![](https://img.shields.io/github/stars/awslabs/deequ.svg?style=social) - A library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
-* [Deep Anomaly Detection with Outlier Exposure](https://github.com/hendrycks/outlier-exposure) ![](https://img.shields.io/github/stars/hendrycks/outlier-exposure.svg?style=social) - Outlier Exposure (OE) is a method for improving anomaly detection performance in deep learning models. [Paper](https://arxiv.org/pdf/1812.04606.pdf)
-* [PyOD](https://github.com/yzhao062/pyod) ![](https://img.shields.io/github/stars/yzhao062/pyod.svg?style=social) - A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).
-* [SUOD](https://github.com/yzhao062/SUOD) ![](https://img.shields.io/github/stars/yzhao062/SUOD.svg?style=social) - SUOD (Scalable Unsupervised Outlier Detection) is an acceleration system for large-scale anomaly/outlier detection.
-* [TextAttack](https://github.com/QData/TextAttack) ![](https://img.shields.io/github/stars/QData/TextAttack.svg?style=social) - TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP.
-* [TFDV](https://github.com/tensorflow/data-validation) ![](https://img.shields.io/github/stars/tensorflow/data-validation.svg?style=social) - TFDV (Tensorflow Data Validation) is a library for exploring and validating machine learning data.
-* [TODS](https://github.com/datamllab/tods) ![](https://img.shields.io/github/stars/datamllab/tods.svg?style=social) - TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.
-
-
 ## AutoML
 * [AutoGluon](https://github.com/autogluon/autogluon) ![](https://img.shields.io/github/stars/autogluon/autogluon.svg?style=social) - Automated feature, model, and hyperparameter selection for tabular, image, and text data on top of popular machine learning libraries (Scikit-Learn, LightGBM, CatBoost, PyTorch, MXNet).
 * [Autokeras](https://github.com/keras-team/autokeras) ![](https://img.shields.io/github/stars/keras-team/autokeras.svg?style=social) - AutoML library for Keras based on ["Auto-Keras: Efficient Neural Architecture Search with Network Morphism"](https://arxiv.org/abs/1806.10282).
@@ -417,6 +404,19 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [Hopsworks Feature Store](https://github.com/logicalclocks/hopsworks) ![](https://img.shields.io/github/stars/logicalclocks/hopsworks.svg?style=social) - Offline/Online Feature Store for ML [(Video)](https://www.youtube.com/watch?v=N1BjPk1smdg).
 
 
+## Industry-strength AD
+* [adtk](https://github.com/arundo/adtk) ![](https://img.shields.io/github/stars/arundo/adtk.svg?style=social)  - A Python toolkit for rule-based/unsupervised anomaly detection in time series.
+* [Alibi Detect](https://github.com/SeldonIO/alibi-detect) ![](https://img.shields.io/github/stars/SeldonIO/alibi-detect.svg?style=social) - alibi-detect is a Python package focused on outlier, adversarial and concept drift detection.
+* [Darts](https://github.com/unit8co/darts) ![](https://img.shields.io/github/stars/unit8co/darts.svg?style=social) - Darts is a library for user-friendly forecasting and anomaly detection on time series.
+* [Deequ](https://github.com/awslabs/deequ) ![](https://img.shields.io/github/stars/awslabs/deequ.svg?style=social) - A library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
+* [Deep Anomaly Detection with Outlier Exposure](https://github.com/hendrycks/outlier-exposure) ![](https://img.shields.io/github/stars/hendrycks/outlier-exposure.svg?style=social) - Outlier Exposure (OE) is a method for improving anomaly detection performance in deep learning models. [Paper](https://arxiv.org/pdf/1812.04606.pdf)
+* [PyOD](https://github.com/yzhao062/pyod) ![](https://img.shields.io/github/stars/yzhao062/pyod.svg?style=social) - A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).
+* [SUOD](https://github.com/yzhao062/SUOD) ![](https://img.shields.io/github/stars/yzhao062/SUOD.svg?style=social) - SUOD (Scalable Unsupervised Outlier Detection) is an acceleration system for large-scale anomaly/outlier detection.
+* [TextAttack](https://github.com/QData/TextAttack) ![](https://img.shields.io/github/stars/QData/TextAttack.svg?style=social) - TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP.
+* [TFDV](https://github.com/tensorflow/data-validation) ![](https://img.shields.io/github/stars/tensorflow/data-validation.svg?style=social) - TFDV (Tensorflow Data Validation) is a library for exploring and validating machine learning data.
+* [TODS](https://github.com/datamllab/tods) ![](https://img.shields.io/github/stars/datamllab/tods.svg?style=social) - TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.
+
+
 ## Industry Strength CV
 * [Deep Lake](https://github.com/activeloopai/deeplake) ![](https://img.shields.io/github/stars/activeloopai/deeplake.svg?style=social) - Deep Lake is a data infrastructure optimized for computer vision.
 * [Detectron2](https://github.com/facebookresearch/detectron2) ![](https://img.shields.io/github/stars/facebookresearch/detectron2.svg?style=social) - Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms.