GitHub

SpiCAE: Spiking Contrastive Learning and Autoencoder for Network Intrusion Detection

A deep learning approach for Network Intrusion Detection utilizing Spiking Neural Networks, Contrastive Learning, Autoencoders, and K-Means clustering. Trained on four benchmark datasets: NSL-KDD, ...
GitHub

Evolutionary Adversarial Autoencoder for Unsupervised Anomaly Detection: Prototype Implementation.

The training objective combines multiple loss components: Total Loss = Reconstruction Loss + β×KL Loss + λ₁×Adversarial Loss(latent) + λ₂×Adversarial Loss(data) -ae_hidden_dim: Hidden layer size for ...
IEEE

Self-Supervised Masked Graph Autoencoder for Hyperspectral Anomaly Detection

Abstract: Hyperspectral image anomaly detection faces the challenge of difficulty in annotating anomalous targets. Autoencoder(AE)-based methods are widely used due to their excellent image ...
Scientific Research Publishing

Traffic Speed Prediction Based on Autoencoder and Deep Learning ()

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...
IEEE

Autoencoder for 4-Dimensional Fiber Orientation Distributions from Diffusion MRI

Abstract: Fiber orientation distributions (FODs) are widely used in connectome analysis based on diffusion MRI. Spherical har-monics (SPHARMs) are often used for the efficient repre-sentation of FODs; ...