Project Page For Semantic Uncertainty Intervals In this work, we provide principled uncertainty intervals that are guaranteed to contain the true semantic factors for any underlying generative model. We use this feature to obtain semantically meaningful uncertainty in the latent space of a generative model. since the latent space is disentangled, the uncertainty intervals natually factor into different interpretable dimensions.
Project Page For Semantic Uncertainty Intervals In this work, we provide principled uncertainty intervals that are guaranteed to contain the true semantic factors for any underlying generative model. Our work is about creating uncertainty intervals in a disentangled latent space and using those to quantify uncertainty on semantic attributes that would otherwise not be possible to represent. In this work, we propose a novel framework that actively learns to generate semantic maps outside the field of view of the agent and leverages the uncertainty over the semantic classes in the unobserved areas to decide on long term goals. This page provides detailed instructions on how to run example experiments using the semantic uncertainty framework and how to evaluate the results. it covers the entire process from executing the pip.
Project Page For Semantic Uncertainty Intervals In this work, we propose a novel framework that actively learns to generate semantic maps outside the field of view of the agent and leverages the uncertainty over the semantic classes in the unobserved areas to decide on long term goals. This page provides detailed instructions on how to run example experiments using the semantic uncertainty framework and how to evaluate the results. it covers the entire process from executing the pip. In this work, we provide principled uncertainty intervals that are guaranteed to contain the true semantic factors for any underlying generative model. Learn how to compute conformal prediction intervals and control risks in various tasks such as regression, classification, time series, and even complex tasks like multi label classification and semantic segmentation. This repository contains the code for our 2023 iclr paper semantic uncertainty: linguistic invariances for uncertainty estimation in natural language generation. The endpoints of these calibrated intervals can then be propagated through the generator to produce interpretable uncertainty visualizations for each semantic factor. this technique reliably communicates semantically meaningful, principled, and instance adaptive uncertainty in inverse problems like image super resolution and image completion.
Project Page For Semantic Uncertainty Intervals In this work, we provide principled uncertainty intervals that are guaranteed to contain the true semantic factors for any underlying generative model. Learn how to compute conformal prediction intervals and control risks in various tasks such as regression, classification, time series, and even complex tasks like multi label classification and semantic segmentation. This repository contains the code for our 2023 iclr paper semantic uncertainty: linguistic invariances for uncertainty estimation in natural language generation. The endpoints of these calibrated intervals can then be propagated through the generator to produce interpretable uncertainty visualizations for each semantic factor. this technique reliably communicates semantically meaningful, principled, and instance adaptive uncertainty in inverse problems like image super resolution and image completion.
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