Score-based Generative Models (SGMs) are lauded for producing high-quality samples from complex data distributions, with empirical success and strong theoretical support. Recent theories provide error bounds for assessing distribution disparity, showing SGMs’ imitation abilities. However, a counter-example challenges their capabilities, illustrating a potential memorization effect and difficulty in generating diverse samples. This raises important considerations for relying solely on the empirical optimal score function in SGMs.
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Understanding Score-based Generative Models (SGMs)
Score-based Generative Models (SGMs) are a powerful tool in generative modeling, capable of producing high-quality samples from complex data distributions. They have shown empirical success, with robust theoretical properties to closely approximate ground truth distributions.
Theoretical Insights and Practical Implications
New theories offer error bounds to gauge the accuracy of SGMs in approximating distributions. This implies their robust ability to imitate ground-truth distributions, especially when the score function is efficiently learned.
New Perspectives and Challenges
Recent research presents counter-examples that highlight potential limitations of SGMs, particularly in generating diverse samples. Despite theoretical backing, SGMs may exhibit a memorization effect, limiting their ability to produce novel instances.
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