Feb 2026· arXiv.org· Vol abs/2602.01419· 0 citations· 28 references
Computer Science
TL;DR
Experiments on small-scale datasets with simulated ground truth across the full data distribution show consistent accuracy gains over baselines, demonstrating the method's effectiveness in data-scarce manufacturing environments.
Abstract
High-level Computer-Aided Process Planning (CAPP) generates manufacturing process plans from part specifications. It suffers from limited dataset availability in industry, reducing model generalization. We propose a semi-supervised learning approach to improve transformer-based CAPP transformer models without manual labeling. An oracle, trained on available transformer behaviour data, filters correct predictions from unseen parts, which are then used for one-shot retraining. Experiments on small-scale datasets with simulated ground truth across the full data distribution show consistent accuracy gains over baselines, demonstrating the method's effectiveness in data-scarce manufacturing environments.
This work proposes a teacher-student semi-supervised learning framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement, and introduces a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments.
Chikao Tsuchiya, Dhaval Bhanderi, David Ilstrup et al.· 0 citations
In semi-supervised object detection (SSOD), due to the limited availability of labeled data, the quality and quantity of pseudo labels generated from unlabeled images are crucial for model training. Our study reveals that in the early stages of training, the number of usable pseudo labels is very low, which hampers the...
Xi Yang, Peng-Hui Li, Nan-Nan Wang· IEEE Transactions on Image P...· 0 citations
In industrial process quality prediction, limited labeled samples and noise interference often degrade the application performance of data-driven models. To address these issues, a Dynamic Co-regression Multi-scale Vision Transformer (DCMSViT) semi-supervised model is proposed to improve quality prediction accuracy in...
Qi Yao, Xiao-Qiang Zhao, Hai-Feng Huo et al.· Measurement science and tech...· 0 citations
By separating different pseudo-label reliability regions into specialized adaptation paths, TriNoL improves robustness to noisy supervision while keeping the training cost low.
Xuan-Yu Liu, Zheng Fang, Hong-Yang He et al.· 2 citations
Weakly supervised semantic segmentation (WSSS) aims to train dense prediction models from inexpensive supervision such as image-level labels. Recent foundation models provide complementary capabilities: promptable segmentation models can produce high-coverage object masks, while self-supervised vision transformers prov...
Duc-Hien Nguyen, L. Nguyễn, X. Nguyễn et al.· International Conference on...· 0 citations
GATTA (Graph Active Learning with Test-Time Augmentation) is introduced, a framework for enhancing active learning by aggregating predictions across multiple augmented views to produce more reliable uncertainty estimates, and generalizes across architectures, and outperforms model-side ensemble methods such as MC Dropo...
Zsombor Bánfi, András Gézsi, András Formanek· 0 citations
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