Practical Solutions and Value of AI in Generative Models
Enhancing Generative Model Performance
Deep generative models can be evaluated using metrics like Fréchet Inception Distance (FID) to ensure consistent performance. Researchers have discovered correlations between geometric descriptors and factors like generation aesthetics, artifacts, uncertainty, and memorization, which can influence the likelihood of generated samples.
Guiding Generative Models
Geometric descriptors such as local scaling can guide generative models to produce varied and detailed outputs. By steering the generative process using classifier guidance to maximize local scaling, models can create sharper, more textured images with higher diversity.
Self-Assessment of Generative Models
Geometry-based descriptors—local scaling, rank, and complexity—can be used for self-assessment of generative models without relying on training data or human evaluators. These descriptors help evaluate the learned manifold’s uncertainty, dimensionality, and smoothness, revealing insights into generation quality, diversity, and biases.
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