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Found 7 papers, 5 papers with code
Deep Anomaly Detection in Text
Deep anomaly detection methods have become increasingly popular in recent years, with methods like Stacked Autoencoders, Variational Autoencoders, and Generative Adversarial Networks greatly improving the state-of-the-art.
Time Series Anomaly Detection using Diffusion-based Models
Our models outperform the baselines on synthetic datasets and are competitive on real-world datasets, illustrating the potential of diffusion-based methods for AD in multivariate time series.
Probabilistically Rewired Message-Passing Neural Networks
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.
VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web
The DarkWeb represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services.
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models.
Ranked #5 on
Unsupervised Anomaly Detection
on AnoShift
Rethinking the Authorship Verification Experimental Setups
One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset.
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers
Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods.
Ranked #1 on
Anomaly Detection
on AG News
