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GitHub5mon
Alessandrovinci/Autoencoder-Explainability - GitHub
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GitHub9mon
GitHub - aulanaufal/anomaly-detection-autoencoder: A notebook to create simple anomaly detection workflow with autoencoder
The Autoencoder is trained on normal operational data, and when it encounters anomalous data, it produces a large reconstruction error, which helps to identify the anomalies. Figure 1: An AI-generated ...
LinkedIn1y
Autoencoders: How They Work and Their Uses in Machine Learning - LinkedIn
For example—consider an autoencoder that has been trained on a specific dataset P. For any image sampled for the training dataset, the autoencoder is bound to give a low reconstruction loss and ...
IEEE8y
A Robust Interval Autoencoder - IEEE Xplore
A new autoencoder dealing with interval-valued or set-valued training data is studied in the paper. The first main idea underlying the autoencoder is based on t ...
Harvard Business School3y
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error - Article - Faculty & Research - Harvard Business School
By: Katherine C. Kellogg, Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon and Karim R. Lakhani ...
Simon Fraser University4y
Interactive Reconstruction of Monte Carlo Image Sequences using a Recurrent Denoising Autoencoder | Pixels & Predictions - Simon Fraser University
This paper proposes a learning-based approach for reconstruction of global illumination with very low sampling budgets (as low as 1 spp) at interactive rates. At 1 sample per pixel (spp), the Monte ...