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 ...
GitHub

MAFS: Masked Autoencoder for Infrared-Visible Image Fusion and Semantic Segmentation

Infrared-visible image fusion methods aim at generating fused images with good visual quality and also facilitate the performance of high-level tasks. Indeed, existing semantic-driven methods have ...
Visual Studio Magazine

Data Anomaly Detection Using LightGBM

Dr. James McCaffrey from Microsoft Research presents a complete program that uses the Python language LightGBM system to create a custom autoencoder for data anomaly detection. You can easily adapt ...
IEEE

Cross-Modal Contrastive Masked AutoEncoder for Compressed Video Pre-Training

Abstract: In this paper, we propose a novel Transformer based approach, namely Cross-modal Contrastive Masked AutoEncoder (C2MAE), to Self-Supervised Learning (SSL) on compressed videos. A unified ...
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 ...