Abstract:
Deep generative models train neural networks to model training sample distributions. Research has fragmented into interconnected approaches that trade run-time, diversity, and architectural restrictions. This compendium covers energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, and many hybrid approaches. Comparing and contrasting these methods, explaining their premises and interrelationships, and reviewing current state-of-the-art advances and implementations.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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