engleski

Applications of generative approaches for artificial intelligence

Most machine learning applications leverage discriminative models and supervised learning. However, discriminative models are unable to generate new content or measure plausibility of the data. Additionally, they are prone to learning simple decision rules which perform well on standard benchmarks but fail in real-world scenarios. Generative approaches are a promising approach towards alleviating these problems due to ability to learn complementary information from the training dataset. We are especially interested in a class of generative approaches that optimizes the data likelihood due to resistance to mode collapse. This presentation shall introduce prominent generative approaches and provide an overview of application fields in computer vision and natural language processing.

computer vision

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