A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction

• URL: http://arxiv.org/abs/2110.03446v1
• Date: Wed, 6 Oct 2021 00:25:22 GMT
• Title: A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction
• Authors: Moitreya Chatterjee and Narendra Ahuja and Anoop Cherian
• Abstract summary: We introduce Neural Uncertainty Quantifier (NUQ) - a principled quantification of the model's predictive uncertainty. Our proposed framework trains more effectively compared to the state-of-theart models.
• Score: 45.6432265855424
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Predicting the future frames of a video is a challenging task, in part due to the underlying stochastic real-world phenomena. Prior approaches to solve this task typically estimate a latent prior characterizing this stochasticity, however do not account for the predictive uncertainty of the (deep learning) model. Such approaches often derive the training signal from the mean-squared error (MSE) between the generated frame and the ground truth, which can lead to sub-optimal training, especially when the predictive uncertainty is high. Towards this end, we introduce Neural Uncertainty Quantifier (NUQ) - a stochastic quantification of the model's predictive uncertainty, and use it to weigh the MSE loss. We propose a hierarchical, variational framework to derive NUQ in a principled manner using a deep, Bayesian graphical model. Our experiments on four benchmark stochastic video prediction datasets show that our proposed framework trains more effectively compared to the state-of-the-art models (especially when the training sets are small), while demonstrating better video generation quality and diversity against several evaluation metrics.

Related papers

• Awareness of uncertainty in classification using a multivariate model and multi-views [1.3048920509133808]
  The proposed model regularizes uncertain predictions, and trains to calculate both the predictions and their uncertainty estimations. Given the multi-view predictions together with their uncertainties and confidences, we proposed several methods to calculate final predictions. The proposed methodology was tested using CIFAR-10 dataset with clean and noisy labels.
  arXiv  Detail & Related papers  (2024-04-16T06:40:51Z)
• Posterior Uncertainty Quantification in Neural Networks using Data Augmentation [3.9860047080844807]
  We show that deep ensembling is a fundamentally mis-specified model class, since it assumes that future data are supported on existing observations only. We propose MixupMP, a method that constructs a more realistic predictive distribution using popular data augmentation techniques. Our empirical analysis showcases that MixupMP achieves superior predictive performance and uncertainty quantification on various image classification datasets.
  arXiv  Detail & Related papers  (2024-03-18T17:46:07Z)
• Predictive Churn with the Set of Good Models [64.05949860750235]
  We study the effect of conflicting predictions over the set of near-optimal machine learning models. We present theoretical results on the expected churn between models within the Rashomon set. We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Multiclass Alignment of Confidence and Certainty for Network Calibration [10.15706847741555]
  Recent studies reveal that deep neural networks (DNNs) are prone to making overconfident predictions. We propose a new train-time calibration method, which features a simple, plug-and-play auxiliary loss known as multi-class alignment of predictive mean confidence and predictive certainty (MACC) Our method achieves state-of-the-art calibration performance for both in-domain and out-domain predictions.
  arXiv  Detail & Related papers  (2023-09-06T00:56:24Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• Post-hoc Uncertainty Learning using a Dirichlet Meta-Model [28.522673618527417]
  We propose a novel Bayesian meta-model to augment pre-trained models with better uncertainty quantification abilities. Our proposed method requires no additional training data and is flexible enough to quantify different uncertainties. We demonstrate our proposed meta-model approach's flexibility and superior empirical performance on these applications.
  arXiv  Detail & Related papers  (2022-12-14T17:34:11Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Stochastic Latent Residual Video Prediction [0.0]
  This paper introduces a novel temporal model whose dynamics are governed in a latent space by a residual update rule. It naturally models video dynamics as it allows our simpler, more interpretable, latent model to outperform prior state-of-the-art methods on challenging datasets.
  arXiv  Detail & Related papers  (2020-02-21T10:44:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.