How do you handle outliers in datasets used for generative AI models especially when they impact training results

Question

Can you name the techniques or methods to handle outliners in datasets used for generative ai models?

Answer 1

Top 5 techniques to handle outliers in datasets used for generative AI models are as follows:

• Z-score and IQR Model: The Z-score and IQR models use statistical boundaries to flag outliers. The Z-score highlights points far from the mean, while the IQR focuses on values outside the typical range.
• Clipping: Sets outlines to a maximum or minimum threshold, preventing extreme values from disrupting analysis.
• Transformation: Applies mathematical adjustments, like a log or square root, to reshaper data and reduce the impact of outliners.
• Isolation Forests Detect outlines by isolating data points. Few splits mean higher isolation and a greater likelihood of being an outlier.
• Autoencoders for Outliner detection: Uses neural networks to learn data patterns, where outliers are identified by their high reconstruction error.

These methods help ensure stable training and improved generative model performance by reducing outliner impact.