Normalizing Flows and Real NVP: How Invertible Neural Networks and Triangular Jacobians Compute Exact Log-Likelihoods
Normalizing Flows and Real NVP: How Invertible Neural Networks and Triangular Jacobians Compute Exact Log-Likelihoods Generative modeling in deep learning revolves around a fundamental question: how can a neural network learn to transform a simple, analytically tractable probability distribution into a complex, high-dimensional empirical data distribution? Over the past decade, four primary generative modeling paradigms have emerged to address this challenge: 1. Generative Adversarial Networ
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