Test Input Validation for Vision-based DL Systems: An Active Learning Approach

• URL: http://arxiv.org/abs/2501.01606v1
• Date: Fri, 03 Jan 2025 02:50:43 GMT
• Title: Test Input Validation for Vision-based DL Systems: An Active Learning Approach
• Authors: Delaram Ghobari, Mohammad Hossein Amini, Dai Quoc Tran, Seunghee Park, Shiva Nejati, Mehrdad Sabetzadeh,
• Abstract summary: Testing deep learning (DL) systems requires extensive and diverse, yet valid, test inputs.<n>We propose a test input validation approach for vision-based DL systems.
• Score: 3.760715803298828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Testing deep learning (DL) systems requires extensive and diverse, yet valid, test inputs. While synthetic test input generation methods, such as metamorphic testing, are widely used for DL testing, they risk introducing invalid inputs that do not accurately reflect real-world scenarios. Invalid test inputs can lead to misleading results. Hence, there is a need for automated validation of test inputs to ensure effective assessment of DL systems. In this paper, we propose a test input validation approach for vision-based DL systems. Our approach uses active learning to balance the trade-off between accuracy and the manual effort required for test input validation. Further, by employing multiple image-comparison metrics, it achieves better results in classifying valid and invalid test inputs compared to methods that rely on single metrics. We evaluate our approach using an industrial and a public-domain dataset. Our evaluation shows that our multi-metric, active learning-based approach produces several optimal accuracy-effort trade-offs, including those deemed practical and desirable by our industry partner. Furthermore, provided with the same level of manual effort, our approach is significantly more accurate than two state-of-the-art test input validation methods, achieving an average accuracy of 97%. Specifically, the use of multiple metrics, rather than a single metric, results in an average improvement of at least 5.4% in overall accuracy compared to the state-of-the-art baselines. Incorporating an active learning loop for test input validation yields an additional 7.5% improvement in average accuracy, bringing the overall average improvement of our approach to at least 12.9% compared to the baselines.

Related papers

• On Speeding Up Language Model Evaluation [48.51924035873411]
  Development of prompt-based methods with Large Language Models (LLMs) requires making numerous decisions. We propose a novel method to address this challenge. We show that it can identify the top-performing method using only 5-15% of the typically needed resources.
  arXiv  Detail & Related papers  (2024-07-08T17:48:42Z)
• Text Quality-Based Pruning for Efficient Training of Language Models [66.66259229732121]
  We propose a novel method for numerically evaluating text quality in large unlabelled NLP datasets. By proposing the text quality metric, the paper establishes a framework to identify and eliminate low-quality text instances. Experimental results over multiple models and datasets demonstrate the efficacy of this approach.
  arXiv  Detail & Related papers  (2024-04-26T18:01:25Z)
• Active Test-Time Adaptation: Theoretical Analyses and An Algorithm [51.84691955495693]
  Test-time adaptation (TTA) addresses distribution shifts for streaming test data in unsupervised settings. We propose the novel problem setting of active test-time adaptation (ATTA) that integrates active learning within the fully TTA setting.
  arXiv  Detail & Related papers  (2024-04-07T22:31:34Z)
• Test Generation Strategies for Building Failure Models and Explaining Spurious Failures [4.995172162560306]
  Test inputs fail not only when the system under test is faulty but also when the inputs are invalid or unrealistic. We propose to build failure models for inferring interpretable rules on test inputs that cause spurious failures. We show that our proposed surrogate-assisted approach generates failure models with an average accuracy of 83%.
  arXiv  Detail & Related papers  (2023-12-09T18:36:15Z)
• Better Practices for Domain Adaptation [62.70267990659201]
  Domain adaptation (DA) aims to provide frameworks for adapting models to deployment data without using labels. Unclear validation protocol for DA has led to bad practices in the literature. We show challenges across all three branches of domain adaptation methodology.
  arXiv  Detail & Related papers  (2023-09-07T17:44:18Z)
• When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study [4.632232395989182]
  Testing Deep Learning (DL) based systems inherently requires large and representative test sets to evaluate whether DL systems generalise beyond their training datasets. Diverse Test Input Generators (TIGs) have been proposed to produce artificial inputs that expose issues of the DL systems by triggering misbehaviours. This paper investigates what extent TIGs can generate valid inputs, according to both automated and human validators.
  arXiv  Detail & Related papers  (2022-12-21T21:10:49Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles [38.23896575179384]
  We propose a principled and practically effective framework that simultaneously addresses the two tasks. One instantiation reduces the estimation error for unsupervised accuracy estimation by at least 70% and improves the F1 score for error detection by at least 4.7%. On iWildCam, one instantiation reduces the estimation error for unsupervised accuracy estimation by at least 70% and improves the F1 score for error detection by at least 4.7%.
  arXiv  Detail & Related papers  (2021-06-29T21:32:51Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Active Testing: Sample-Efficient Model Evaluation [39.200332879659456]
  We introduce active testing: a new framework for sample-efficient model evaluation. Active testing addresses this by carefully selecting the test points to label. We show how to remove that bias while reducing the variance of the estimator.
  arXiv  Detail & Related papers  (2021-03-09T10:20:49Z)
• Distribution-Aware Testing of Neural Networks Using Generative Models [5.618419134365903]
  The reliability of software that has a Deep Neural Network (DNN) as a component is urgently important. We show that three recent testing techniques generate significant number of invalid test inputs. We propose a technique to incorporate the valid input space of the DNN model under test in the test generation process.
  arXiv  Detail & Related papers  (2021-02-26T17:18:21Z)
• Fast Uncertainty Quantification for Deep Object Pose Estimation [91.09217713805337]
  Deep learning-based object pose estimators are often unreliable and overconfident. In this work, we propose a simple, efficient, and plug-and-play UQ method for 6-DoF object pose estimation.
  arXiv  Detail & Related papers  (2020-11-16T06:51:55Z)

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.