@ericyocam6Shipped: GAN + Monte Carlo — AI Image Anomaly Detection
An open-source AI research proof of concept demonstrating how Generative Adversarial Networks (GANs) and Monte Carlo simulation can be combined to identify anomalies in image datasets.
The project trains a GAN on the MNIST handwritten digit dataset to learn visual patterns and generate synthetic images. A neural network discriminator assigns scores to images, while Monte Carlo sampling estimates the distribution of those scores to establish statistical thresholds for identifying potentially anomalous inputs.
Built with TensorFlow, Keras, and Python, the experiment integrates deep learning, generative modeling, probabilistic simulation, and GPU-accelerated training. It produces visual comparisons of normal and anomalous images, anomaly-score histograms, and statistical summaries to help explain the detection process.
The framework explores potential applications in AI security, image anomaly detection, quality inspection, and abnormal-pattern recognition. It provides an educational and reproducible foundation for investigating hybrid generative AI and statistical detection techniques.
This is an experimental proof of concept rather than a production-validated anomaly detection system.
Comments2
The side-by-side pictures of normal and odd handwritten digits, plus the chart of scores, let a student or a quality inspector see exactly why something got flagged. That kind of plain explanation is often what gets an inspection team to trust a tool enough to try it on their own parts.
Thanks, Ryan. I agree. Making AI model decisions interpretable is important, particularly in applications like quality inspection where users need to understand why something was flagged. The combination of GAN-based anomaly scoring and Monte Carlo simulation helps visualize those decisions rather than simply producing a classification. I also see value in using this approach as an educational tool to demonstrate how generative AI and statistical methods can work together. Appreciate the feedback!
2 comments
The side-by-side pictures of normal and odd handwritten digits, plus the chart of scores, let a student or a quality inspector see exactly why something got flagged. That kind of plain explanation is often what gets an inspection team to trust a tool enough to try it on their own parts.
Thanks, Ryan. I agree. Making AI model decisions interpretable is important, particularly in applications like quality inspection where users need to understand why something was flagged. The combination of GAN-based anomaly scoring and Monte Carlo simulation helps visualize those decisions rather than simply producing a classification. I also see value in using this approach as an educational tool to demonstrate how generative AI and statistical methods can work together. Appreciate the feedback!