SAGEGAN: Style-Based Anomaly Detection with Gaussian Embeddings using Generative Adversarial Networks

arXiv:2609.27357 · cs.CR · Submitted 2026-09-23 · Read on arXiv

cs.CR

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

Explainability — 19× in this paper · explained in 3 episodes
Accuracy — 17× in this paper · explained in 1 episode
AUC — 15× in this paper · explained in 2 episodes
Consistency — 12× in this paper · explained in 3 episodes
Benchmark — 9× in this paper · explained in 2 episodes
Alignment — 8× in this paper · explained in 1 episode
Zero-Shot Transfer — 6× in this paper · explained in 4 episodes
Embeddings — 4× in this paper · explained in 2 episodes
Latent Space — 4× in this paper · explained in 7 episodes
Adversarial Training — 3× in this paper · explained in 1 episode
Regularization — 3× in this paper · explained in 3 episodes
Robustness — 3× in this paper · explained in 5 episodes
Adam — 2× in this paper · explained in 2 episodes
Generative Model — 2× in this paper · explained in 2 episodes
Generative Models — 2× in this paper · explained in 3 episodes
Interpretability — 2× in this paper · explained in 15 episodes
Adaptation — 1× in this paper · explained in 1 episode
Conference on Computer Vision and Pattern Recognition — 1× in this paper
Covariate Shift — 1× in this paper · explained in 2 episodes
Distribution Shift — 1× in this paper · explained in 6 episodes
Domain Adaptation — 1× in this paper · explained in 5 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Hilbert Space — 1× in this paper · explained in 2 episodes
Reconstruction Error — 1× in this paper · explained in 2 episodes
Trace — 1× in this paper · explained in 2 episodes

Terminology

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