Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets

• URL: http://arxiv.org/abs/2405.16082v1
• Date: Sat, 25 May 2024 06:25:24 GMT
• Title: Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets
• Authors: Hyekyoung Hwang, Jitae Shin,
• Abstract summary: We propose To-hull Uncertainty and Closure Ratio, which measures an uncertainty of trained model based on the convex hull of training data. It can observe the positional relation between the convex hull of the learned data and an unseen sample and infer how extrapolate the sample is from the convex hull.
• Score: 0.13265175299265505
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep Learning (DL) has made remarkable achievements in computer vision and adopted in safety critical domains such as medical imaging or autonomous drive. Thus, it is necessary to understand the uncertainty of the model to effectively reduce accidents and losses due to misjudgment of the Deep Neural Networks (DNN). This can start by efficiently selecting data that could potentially malfunction to the model. Traditionally, data collection and labeling have been done manually, but recently test data selection methods have emerged that focus on capturing samples that are not relevant to what the model had been learned. They're selected based on the activation pattern of neurons in DNN, entropy minimization based on softmax output of the DL. However, these methods cannot quantitatively analyze the extent to which unseen samples are extrapolated from the training data. Therefore, we propose To-hull Uncertainty and Closure Ratio, which measures an uncertainty of trained model based on the convex hull of training data. It can observe the positional relation between the convex hull of the learned data and an unseen sample and infer how extrapolate the sample is from the convex hull. To evaluate the proposed method, we conduct empirical studies on popular datasets and DNN models, compared to state-of-the art test selection metrics. As a result of the experiment, the proposed To-hull Uncertainty is effective in finding samples with unusual patterns (e.g. adversarial attack) compared to the existing test selection metric.

Related papers

• Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation [9.950524371154394]
  We propose a new misspecification measure that can be trained in an unsupervised fashion and reliably detects model misspecification at test time. We show how the proposed misspecification test warns users about suspicious outputs, raises an alarm when predictions are not trustworthy, and guides model designers in their search for better simulators.
  arXiv  Detail & Related papers  (2024-06-05T11:30:16Z)
• Hypothesis-Driven Deep Learning for Out of Distribution Detection [0.8191518216608217]
  We propose a hypothesis-driven approach to quantify whether a new sample is InD or OoD. We adapt our method to detect an unseen sample of bacteria to a trained deep learning model, and show that it reveals interpretable differences between InD and OoD latent responses.
  arXiv  Detail & Related papers  (2024-03-21T01:06:47Z)
• Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
  Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
  arXiv  Detail & Related papers  (2024-01-18T08:12:23Z)
• Continual Test-time Domain Adaptation via Dynamic Sample Selection [38.82346845855512]
  This paper proposes a Dynamic Sample Selection (DSS) method for Continual Test-time Domain Adaptation (CTDA) We apply joint positive and negative learning on both high- and low-quality samples to reduce the risk of using wrong information. Our approach is also evaluated in the 3D point cloud domain, showcasing its versatility and potential for broader applicability.
  arXiv  Detail & Related papers  (2023-10-05T06:35:21Z)
• Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning [22.410220040736235]
  We present a theoretically optimal solution for addressing both coreset selection and active learning. Our proposed method, COPS, is designed to minimize the expected loss of a model trained on subsampled data.
  arXiv  Detail & Related papers  (2023-09-05T14:06:33Z)
• Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning [69.81438976273866]
  Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers) We introduce evidential deep learning (EDL) as an outlier detector to quantify different types of uncertainty, and design different uncertainty metrics for self-training and inference. We propose a novel adaptive negative optimization strategy, making EDL more tailored to the unlabeled dataset containing both inliers and outliers.
  arXiv  Detail & Related papers  (2023-03-21T09:07:15Z)
• Temporal Output Discrepancy for Loss Estimation-based Active Learning [65.93767110342502]
  We present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. Our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2022-12-20T19:29:37Z)
• Contextual Dropout: An Efficient Sample-Dependent Dropout Module [60.63525456640462]
  Dropout has been demonstrated as a simple and effective module to regularize the training process of deep neural networks. We propose contextual dropout with an efficient structural design as a simple and scalable sample-dependent dropout module. Our experimental results show that the proposed method outperforms baseline methods in terms of both accuracy and quality of uncertainty estimation.
  arXiv  Detail & Related papers  (2021-03-06T19:30:32Z)
• Statistical model-based evaluation of neural networks [74.10854783437351]
  We develop an experimental setup for the evaluation of neural networks (NNs) The setup helps to benchmark a set of NNs vis-a-vis minimum-mean-square-error (MMSE) performance bounds. This allows us to test the effects of training data size, data dimension, data geometry, noise, and mismatch between training and testing conditions.
  arXiv  Detail & Related papers  (2020-11-18T00:33:24Z)
• Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling [3.077929914199468]
  We show that modifying the sampling distributions for dropout layers in neural networks improves the quality of uncertainty estimation. Our main idea consists of two main steps: computing data-driven correlations between neurons and generating samples, which include maximally diverse neurons.
  arXiv  Detail & Related papers  (2020-03-06T15:20:04Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

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.