This paper explores the importance of modeling both aleatoric and epistemic uncertainties in Bayesian deep learning for computer vision tasks. Aleatoric uncertainty captures noise inherent in observations, while epistemic uncertainty reflects uncertainty in the model parameters. The authors propose a Bayesian deep learning framework that combines input-dependent aleatoric uncertainty with epistemic uncertainty, enabling the modeling of both types of uncertainty in vision tasks such as semantic segmentation and depth regression. They show that modeling aleatoric uncertainty leads to new loss functions that can be interpreted as learned attenuation, making the loss more robust to noisy data and improving performance on segmentation and depth regression benchmarks. The paper discusses the distinction between aleatoric and epistemic uncertainties, highlighting that aleatoric uncertainty is inherent in the data and cannot be reduced with more data, while epistemic uncertainty can be reduced with more data. The authors demonstrate that modeling both uncertainties together can lead to better performance than modeling either uncertainty alone. They also show that aleatoric uncertainty can be interpreted as learned loss attenuation, which helps the model be more robust to noisy data. The authors evaluate their framework on two tasks: semantic segmentation and depth regression. They show that their approach improves performance on both tasks, with the best results achieved when both aleatoric and epistemic uncertainties are modeled. They also show that aleatoric uncertainty is more important in large data settings, while epistemic uncertainty is more important in safety-critical applications where the model must understand examples that differ from the training data. The paper concludes that combining aleatoric and epistemic uncertainties in Bayesian deep learning can lead to new state-of-the-art results on computer vision tasks. The authors emphasize the importance of modeling uncertainty in deep learning to improve the robustness and reliability of models, particularly in safety-critical applications.This paper explores the importance of modeling both aleatoric and epistemic uncertainties in Bayesian deep learning for computer vision tasks. Aleatoric uncertainty captures noise inherent in observations, while epistemic uncertainty reflects uncertainty in the model parameters. The authors propose a Bayesian deep learning framework that combines input-dependent aleatoric uncertainty with epistemic uncertainty, enabling the modeling of both types of uncertainty in vision tasks such as semantic segmentation and depth regression. They show that modeling aleatoric uncertainty leads to new loss functions that can be interpreted as learned attenuation, making the loss more robust to noisy data and improving performance on segmentation and depth regression benchmarks. The paper discusses the distinction between aleatoric and epistemic uncertainties, highlighting that aleatoric uncertainty is inherent in the data and cannot be reduced with more data, while epistemic uncertainty can be reduced with more data. The authors demonstrate that modeling both uncertainties together can lead to better performance than modeling either uncertainty alone. They also show that aleatoric uncertainty can be interpreted as learned loss attenuation, which helps the model be more robust to noisy data. The authors evaluate their framework on two tasks: semantic segmentation and depth regression. They show that their approach improves performance on both tasks, with the best results achieved when both aleatoric and epistemic uncertainties are modeled. They also show that aleatoric uncertainty is more important in large data settings, while epistemic uncertainty is more important in safety-critical applications where the model must understand examples that differ from the training data. The paper concludes that combining aleatoric and epistemic uncertainties in Bayesian deep learning can lead to new state-of-the-art results on computer vision tasks. The authors emphasize the importance of modeling uncertainty in deep learning to improve the robustness and reliability of models, particularly in safety-critical applications.