While machine learning ... paper introduces Anomaly-Flow, a novel framework that addresses this critical gap by combining Federated Learning (FL) with Generative Adversarial Networks (GANs) for ...

This project is my attempt to demystify anomaly detection in network traffic using machine learning. It's a self-guided research-backed implementation aimed at combining my knowledge of Python, ...

Abstract: Graph anomaly detection has gained significant research interest across various domains. Due to the lack of labeled data, contrastive learning has been applied in detecting anomalies and ...

Explore how unsupervised learning is a game-changer for anomaly detection in data science, identifying outliers and unexpected patterns.

Imagine you're sifting through a mountain of data, searching for those rare, valuable insights that can transform your business strategy. That's where unsupervised learning comes into play ...

[paper] [code] [Xu2025] Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models in CVPR, 2025. [paper] [code] [Qu2025] Bayesian Prompt Flow Learning for Zero-Shot ...

Anomaly detection plays a critical role in identifying and addressing ... drones equipped with multispectral cameras can capture crop health data, and machine learning algorithms can predict optimal ...

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection ... With the emergence of advanced machine learning, ...