Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification

• URL: http://arxiv.org/abs/2101.00982v2
• Date: Thu, 28 Jan 2021 14:32:24 GMT
• Title: Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
• Authors: Michael Weiss and Paolo Tonella
• Abstract summary: Uncertainty-wizard is a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and easy to understand interface.
• Score: 4.56877715768796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervision.We present uncertainty-wizard, a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and easy to understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.

Related papers

• Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification [11.898587151486709]
  Uncertainty Quantification (UQ) for Deep Learning aims to improve the reliability of uncertainty estimates.<n>We introduce Torch-Uncertainty, a PyTorch and Lightning-based framework designed to streamline training and evaluation.<n>We present comprehensive experimental results that benchmark a diverse set of UQ methods across classification, segmentation, and regression tasks.
  arXiv  Detail & Related papers  (2025-11-13T13:12:52Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• Uncertainty-Aware Decomposed Hybrid Networks [5.67478985222587]
  We propose a hybrid approach that combines the adaptability of neural networks with the interpretability, transparency, and robustness of domain-specific operators. Our method decomposes the recognition into multiple task-specific operators that focus on different characteristics, supported by a novel confidence measurement.
  arXiv  Detail & Related papers  (2025-03-24T19:30:17Z)
• Visual Agents as Fast and Slow Thinkers [88.6691504568041]
  We introduce FaST, which incorporates the Fast and Slow Thinking mechanism into visual agents. FaST employs a switch adapter to dynamically select between System 1/2 modes. It tackles uncertain and unseen objects by adjusting model confidence and integrating new contextual data.
  arXiv  Detail & Related papers  (2024-08-16T17:44:02Z)
• Semi-Supervised Multi-Task Learning Based Framework for Power System Security Assessment [0.0]
  This paper develops a novel machine learning-based framework using Semi-Supervised Multi-Task Learning (SS-MTL) for power system dynamic security assessment. The learning algorithm underlying the proposed framework integrates conditional masked encoders and employs multi-task learning for classification-aware feature representation. Various experiments on the IEEE 68-bus system were conducted to validate the proposed method.
  arXiv  Detail & Related papers  (2024-07-11T22:42:53Z)
• Bridging the Gap Between End-to-End and Two-Step Text Spotting [88.14552991115207]
  Bridging Text Spotting is a novel approach that resolves the error accumulation and suboptimal performance issues in two-step methods. We demonstrate the effectiveness of the proposed method through extensive experiments.
  arXiv  Detail & Related papers  (2024-04-06T13:14:04Z)
• SURE: SUrvey REcipes for building reliable and robust deep networks [12.268921703825258]
  In this paper, we revisit techniques for uncertainty estimation within deep neural networks and consolidate a suite of techniques to enhance their reliability. We rigorously evaluate SURE against the benchmark of failure prediction, a critical testbed for uncertainty estimation efficacy. When applied to real-world challenges, such as data corruption, label noise, and long-tailed class distribution, SURE exhibits remarkable robustness, delivering results that are superior or on par with current state-of-the-art specialized methods.
  arXiv  Detail & Related papers  (2024-03-01T13:58:19Z)
• DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation [11.099838952805325]
  Quantifying the predictive uncertainty is a promising endeavour to open up the use of deep neural networks for such applications. We present a novel approach for efficient and reliable uncertainty estimation which we call Deep Uncertainty Distillation using Ensembles (DUDES) DUDES applies student-teacher distillation with a Deep Ensemble to accurately approximate predictive uncertainties with a single forward pass.
  arXiv  Detail & Related papers  (2023-03-17T08:56:27Z)
• Higher-order accurate two-sample network inference and network hashing [13.984114642035692]
  Two-sample hypothesis testing for network comparison presents many significant challenges. We develop a comprehensive toolbox featuring a novel main method and its variants. Our method outperforms existing tools in speed and accuracy, and it is proved power-optimal.
  arXiv  Detail & Related papers  (2022-08-16T07:31:11Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System [78.60415450507706]
  Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
  arXiv  Detail & Related papers  (2021-07-28T10:28:05Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search [94.80212602202518]
  We propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS) We employ a one-shot architecture search approach in order to obtain a reduced search cost. We achieve state-of-the-art results in terms of accuracy-speed trade-off.
  arXiv  Detail & Related papers  (2020-09-29T11:56:01Z)
• Confidence-Aware Learning for Deep Neural Networks [4.9812879456945]
  We propose a method of training deep neural networks with a novel loss function, named Correctness Ranking Loss. It regularizes class probabilities explicitly to be better confidence estimates in terms of ordinal ranking according to confidence. It has almost the same computational costs for training as conventional deep classifiers and outputs reliable predictions by a single inference.
  arXiv  Detail & Related papers  (2020-07-03T02:00:35Z)

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.