Have I Solved This Before? Retrieving Similar Segmentation Problems for Evolutionary Learning

• URL: http://arxiv.org/abs/2606.08155v1
• Date: Sat, 06 Jun 2026 13:10:45 GMT
• Title: Have I Solved This Before? Retrieving Similar Segmentation Problems for Evolutionary Learning
• Abstract summary: This work investigates a research direction that deviates from the traditional monitoring-system development process.<n>In contrast to traditional design cycles, this paper proposes to gradually collect knowledge and store it in an abstract system model.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Reliable integration and solid configuration of monitoring systems constitute a fundamental prerequisites for achieving high efficiency and productivity in contemporary manufacturing environments. Design decisions on sensor type and system architecture have to be made at an early stage and under comparably high uncertainty. This work investigates a research direction that deviates from the traditional monitoring-system development process by shifting the attention from algorithm design to a deeper analysis of the inspection problem. In contrast to traditional design cycles, this paper proposes to gradually collect knowledge and store it in an abstract system model. This enables the retrieval of similar solutions for future use cases, preventing the need for expensive model training from scratch and allowing instead for the incremental refinement of existing base configurations. Reuse of previously generated pipelines reduces the risk of late and costly revisions. As there is little knowledge on cross-domain transferability of filter pipelines, this study analyzes the potential of retrieving filter pipelines to transfer them to different but similar segmentation problems. Finally, we statistically analyze the benefits of this `transfer learning' variant which is predominantly applied to image segmentation problems. In addition, we discuss how simple models help balancing the trade-off between complexity, technical requirements, and reliability in the design process.

Related papers

• Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations [98.44542103979735]
  First-order gradient descent methods, epitomized by gradient descent (SGD) and Adam, serve as the cornerstone of modern training pipelines.<n>Large-scale model training, stringent privacy requirements, and distributed learning paradigms expose critical limitations in these conventional approaches regarding privacy protection and memory efficiency.<n>We retrospectively analyze the evolutionary trajectory of deep learning optimization algorithms and present a comprehensive empirical evaluation of mainstreams across diverse model architectures and training scenarios.<n>We distill key emerging trends and fundamental design trade-offs, pinpointing promising directions for future research.
  arXiv  Detail & Related papers  (2026-04-14T17:01:36Z)
• Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
  Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
  arXiv  Detail & Related papers  (2026-03-11T09:23:20Z)
• LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization [7.8639568562295965]
  We consider small-data, large-scale decision problems in which a firm must make many operational decisions simultaneously.<n>We propose a pretrain-then-finetune approach built on a designed Transformer model to address this challenge.
  arXiv  Detail & Related papers  (2026-02-03T16:08:33Z)
• Combining SHAP and Causal Analysis for Interpretable Fault Detection in Industrial Processes [1.924423011183876]
  This study tackles such difficulties using the Tennessee Eastman Process, a well-established benchmark known for its intricate dynamics.<n>We transform the problem into a more manageable and transparent form, pinpointing the most critical process features driving fault predictions.<n>The resulting causal structures align strikingly with SHAP findings, consistently highlighting key process elements-like cooling and separation systems-as pivotal to fault development.
  arXiv  Detail & Related papers  (2025-10-27T19:56:46Z)
• Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction [57.19302613163439]
  We introduce neural network reprogrammability as a unifying framework for model adaptation.<n>We present a taxonomy that categorizes such information manipulation approaches across four key dimensions.<n>We also analyze remaining technical challenges and ethical considerations.
  arXiv  Detail & Related papers  (2025-06-05T05:42:27Z)
• SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era [0.8866016545928136]
  We argue for the urgent need to establish a structured reporting standard for surrogate models.<n>By promoting a standardised yet flexible framework, we aim to improve the reliability of surrogate modelling.
  arXiv  Detail & Related papers  (2025-02-10T18:31:15Z)
• Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems [92.89673285398521]
  o1-like reasoning systems have demonstrated remarkable capabilities in solving complex reasoning tasks.<n>We introduce an imitate, explore, and self-improve'' framework to train the reasoning model.<n>Our approach achieves competitive performance compared to industry-level reasoning systems.
  arXiv  Detail & Related papers  (2024-12-12T16:20:36Z)
• The Significance of Latent Data Divergence in Predicting System Degradation [1.2058600649065616]
  Condition-Based Maintenance is pivotal in enabling the early detection of potential failures in engineering systems. We introduce a novel methodology grounded in the analysis of statistical similarity within latent data from system components. We infer the similarity between systems by evaluating the divergence of these priors, offering a nuanced understanding of individual system behaviors.
  arXiv  Detail & Related papers  (2024-06-13T11:41:20Z)
• Machine learning for structural design models of continuous beam systems via influence zones [3.284878354988896]
  This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. The aim of this approach is to conceptualise a non-iterative structural design model that predicts cross-section requirements for continuous beam systems of arbitrary system size.
  arXiv  Detail & Related papers  (2024-03-14T14:53:18Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• On Robustness and Transferability of Convolutional Neural Networks [147.71743081671508]
  Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. We study the interplay between out-of-distribution and transfer performance of modern image classification CNNs for the first time. We find that increasing both the training set and model sizes significantly improve the distributional shift robustness.
  arXiv  Detail & Related papers  (2020-07-16T18:39:04Z)

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.