Task Specific Prior

Task-specific priors aim to improve machine learning model performance by incorporating prior knowledge relevant to a particular task, overcoming limitations of generic priors and enhancing learning, especially with limited data. Current research focuses on integrating these priors into various architectures, including diffusion models, variational autoencoders, and transformers, often leveraging pre-trained language models or learning task-dependent priors within Bayesian frameworks. This approach shows promise in improving performance across diverse applications such as image restoration, few-shot learning, and causal inference, by guiding model learning towards solutions consistent with existing knowledge.