Tootfinder

Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Guangyu Sun, Jingtao Li, Weiming Zhuang, Chen Chen, Chen Chen, Lingjuan Lyu
https://arxiv.org/abs/2508.16568

Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulations, cloud-based FMs cannot directly access private edge data, limiting their adaptation. Federated learning (FL) provides a privacy-aware alternative, but existing FL approaches overlook the constraints imposed by edge devices -- namely, limited computational resources and the scarcity of labeled data. To address these…

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
Xiaoran Xua, In-Ho Rab, Ravi Sankarc
https://arxiv.org/abs/2507.16845 https://arxiv.o…

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
Lung diseases, including lung cancer and COPD, are significant health concerns globally. Traditional diagnostic methods can be costly, time-consuming, and invasive. This study investigates the use of semi supervised learning methods for lung sound signal detection using a model combination of MFCC+CNN. By introducing semi supervised learning modules such as Mix Match, Co-Refinement, and Co Refurbishing, we aim to enhance the detection performance while reducing dependence on manual annotations.…

Semi-Supervised Learning with Online Knowledge Distillation for Skin Lesion Classification
Siyamalan Manivannan
https://arxiv.org/abs/2508.11511 https://ar…

Semi-Supervised Learning with Online Knowledge Distillation for Skin Lesion Classification
Deep Learning has emerged as a promising approach for skin lesion analysis. However, existing methods mostly rely on fully supervised learning, requiring extensive labeled data, which is challenging and costly to obtain. To alleviate this annotation burden, this study introduces a novel semi-supervised deep learning approach that integrates ensemble learning with online knowledge distillation for enhanced skin lesion classification. Our methodology involves training an ensemble of convolutional…

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
https://arxiv.org/abs/2507.14072

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution, but the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained by the scarcity of labeled spectroscopic data. We aim to develop a scalable, label-efficient method for astronomical classification by leveraging semi-supervised learning (SSL) to overcome the limitations of fully supervised approaches. We propose a novel SSL fram…

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Eliott Thomas, Mickael Coustaty, Aurelie Joseph, Gaspar Deloin, Elodie Carel, Vincent Poulain D'Andecy, Jean-Marc Ogier
https://arxiv.org/abs/2506.14568

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipelines. While semi-supervised learning (SSL) can leverage unlabeled data, existing methods rely on confidence scores that poorly reflect extraction quality. We propose QUEST, a Quality-aware Semi-supervised Table extraction framework designed for business documents. QUEST introduces a novel quality assessment model that ev…

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
Yufeng Luo, Adam D. Myers, Alex Drlica-Wagner, Dario Dematties, Salma Borchani, Frank Valdes, Arjun Dey, David Schlegel, Rongpu Zhou, DESI Legacy Imaging Surveys Team
https://arxiv.org/abs/2507.12784

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., $E(B-V)<0.04$). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learnin…

Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Tao Zhou, Huazhu Fu, Yizhe Zhang, Yi Zhou, Xiao-Jun Wu
https://arxiv.org/abs/2508.09014

Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation
Semi-supervised learning has gained considerable popularity in medical image segmentation tasks due to its capability to reduce reliance on expert-examined annotations. Several mean-teacher (MT) based semi-supervised methods utilize consistency regularization to effectively leverage valuable information from unlabeled data. However, these methods often heavily rely on the student model and overlook the potential impact of cognitive biases within the model. Furthermore, some methods employ co-tr…

SSSUMO: Real-Time Semi-Supervised Submovement Decomposition
Evgenii Rudakov, Jonathan Shock, Otto Lappi, Benjamin Ultan Cowley
https://arxiv.org/abs/2507.08028

SSSUMO: Real-Time Semi-Supervised Submovement Decomposition
This paper introduces a SSSUMO, semi-supervised deep learning approach for submovement decomposition that achieves state-of-the-art accuracy and speed. While submovement analysis offers valuable insights into motor control, existing methods struggle with reconstruction accuracy, computational cost, and validation, due to the difficulty of obtaining hand-labeled data. We address these challenges using a semi-supervised learning framework. This framework learns from synthetic data, initially gene…

Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
Hamed Gholipour, Farid Bozorgnia, Hamzeh Mohammadigheymasi, Kailash Hambarde, Javier Mancilla, Hugo Proenca, Joao Neves, Moharram Challenger
https://arxiv.org/abs/2508.02054

Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
This paper develops a hybrid quantum approach for graph-based semi-supervised learning to enhance performance in scenarios where labeled data is scarce. We introduce two enhanced quantum models, the Improved Laplacian Quantum Semi-Supervised Learning (ILQSSL) and the Improved Poisson Quantum Semi-Supervised Learning (IPQSSL), that incorporate advanced label propagation strategies within variational quantum circuits. These models utilize QR decomposition to embed graph structure directly into qu…

Data augmentation enables label-specific generation of homologous protein sequences
Lorenzo Rosset, Martin Weigt, Francesco Zamponi
https://arxiv.org/abs/2507.15651

Data augmentation enables label-specific generation of homologous protein sequences
Accurately annotating and controlling protein function from sequence data remains a major challenge, particularly within homologous families where annotated sequences are scarce and structural variation is minimal. We present a two-stage approach for semi-supervised functional annotation and conditional sequence generation in protein families using representation learning. First, we demonstrate that protein language models, pretrained on large and diverse sequence datasets and possibly finetune…

Contrastive-KAN: A Semi-Supervised Intrusion Detection Framework for Cybersecurity with scarce Labeled Data
Mohammad Alikhani, Reza Kazemi
https://arxiv.org/abs/2507.10808

Contrastive-KAN: A Semi-Supervised Intrusion Detection Framework for Cybersecurity with scarce Labeled Data
In the era of the Fourth Industrial Revolution, cybersecurity and intrusion detection systems are vital for the secure and reliable operation of IoT and IIoT environments. A key challenge in this domain is the scarcity of labeled cyber-attack data, as most industrial systems operate under normal conditions. This data imbalance, combined with the high cost of annotation, hinders the effective training of machine learning models. Moreover, rapid detection of attacks is essential, especially in cr…

End-to-end Acoustic-linguistic Emotion and Intent Recognition Enhanced by Semi-supervised Learning
Zhao Ren, Rathi Adarshi Rammohan, Kevin Scheck, Sheng Li, Tanja Schultz
https://arxiv.org/abs/2507.07806

End-to-end Acoustic-linguistic Emotion and Intent Recognition Enhanced by Semi-supervised Learning
Emotion and intent recognition from speech is essential and has been widely investigated in human-computer interaction. The rapid development of social media platforms, chatbots, and other technologies has led to a large volume of speech data streaming from users. Nevertheless, annotating such data manually is expensive, making it challenging to train machine learning models for recognition purposes. To this end, we propose applying semi-supervised learning to incorporate a large scale of unlab…

SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation
Guido Manni, Clemente Lauretti, Loredana Zollo, Paolo Soda
https://arxiv.org/abs/2508.06429

SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation
Deep learning has revolutionized medical imaging, but its effectiveness is severely limited by insufficient labeled training data. This paper introduces a novel GAN-based semi-supervised learning framework specifically designed for low labeled-data regimes, evaluated across settings with 5 to 50 labeled samples per class. Our approach integrates three specialized neural networks -- a generator for class-conditioned image translation, a discriminator for authenticity assessment and classificatio…

SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Cheng Wang, Siqi Chen, Donghua Mi, Yang Chen, Yudong Zhang, Yinsheng Li
https://arxiv.org/abs/2506.10325

SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Recent advances in medical imaging have established deep learning-based segmentation as the predominant approach, though it typically requires large amounts of manually annotated data. However, obtaining annotations for intracranial hemorrhage (ICH) remains particularly challenging due to the tedious and costly labeling process. Semi-supervised learning (SSL) has emerged as a promising solution to address the scarcity of labeled data, especially in volumetric medical image segmentation. Unlike …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/7]:
- A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series...
Yijun Lin, Yao-Yi Chiang

Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach
Chiara Faccio, Margherita Porcelli, Francesco Rinaldi, Martin Stoll
https://arxiv.org/abs/2508.09738

Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach
Graph-based machine learning has seen an increased interest over the last decade with many connections to other fields of applied mathematics. Learning based on partial differential equations, such as the phase-field Allen-Cahn equation, allows efficient handling of semi-supervised learning approaches on graphs. The numerical solution of the graph Allen-Cahn equation via a convexity splitting or the Merriman-Bence-Osher (MBO) scheme, albeit being a widely used approach, requires the calculation…

Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Mohammad R. Salmanpour, Amir Hossein Pouria, Sonia Falahati, Shahram Taeb, Somayeh Sadat Mehrnia, Ali Fathi Jouzdani, Mehrdad Oveisi, Ilker Hacihaliloglu, Arman Rahmim
https://arxiv.org/abs/2507.0818…

Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Background: CT imaging is vital for lung cancer management, offering detailed visualization for AI-based prognosis. However, supervised learning SL models require large labeled datasets, limiting their real-world application in settings with scarce annotations. Methods: We analyzed CT scans from 977 patients across 12 datasets extracting 1218 radiomics features using Laplacian of Gaussian and wavelet filters via PyRadiomics Dimensionality reduction was applied with 56 feature selection and ex…

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Zhuocheng Liu, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Zheng Lei, Jiong Jin
https://arxiv.org/abs/2507.22339

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Low Earth Orbit (LEO) satellites are emerging as key components of 6G networks, with many already deployed to support large-scale Earth observation and sensing related tasks. Federated Learning (FL) presents a promising paradigm for enabling distributed intelligence in these resource-constrained and dynamic environments. However, achieving reliable convergence, while minimizing both processing time and energy consumption, remains a substantial challenge, particularly in heterogeneous and partia…

Semi-supervised learning for linear extremile regression
Rong Jiang, Keming Yu, Jiangfeng Wang
https://arxiv.org/abs/2507.01314 https://

Semi-supervised learning for linear extremile regression
Extremile regression, as a least squares analog of quantile regression, is potentially useful tool for modeling and understanding the extreme tails of a distribution. However, existing extremile regression methods, as nonparametric approaches, may face challenges in high-dimensional settings due to data sparsity, computational inefficiency, and the risk of overfitting. While linear regression serves as the foundation for many other statistical and machine learning models due to its simplicity, …

Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
Konstantin Avrachenkov, Diego Goldsztajn
https://arxiv.org/abs/2506.09223 htt…

Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
We consider graphs with two communities and analyze an algorithm for learning the community labels when the edges of the graph and only a small fraction of the labels are known in advance. The algorithm is based on the Glauber dynamics for an Ising model where the energy function includes a quadratic penalty on the magnetization. The analysis focuses on graphs sampled from a Stochastic Block Model (SBM) with slowly growing mean degree. We derive a mean-field limit for the magnetization of each …

ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds
Shanle Yao, Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
https://arxiv.org/abs/2508.09058

ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds
Video Anomaly Detection (VAD) can play a key role in spotting unusual activities in video footage. VAD is difficult to use in real-world settings due to the dynamic nature of human actions, environmental variations, and domain shifts. Traditional evaluation metrics often prove inadequate for such scenarios, as they rely on static assumptions and fall short of identifying a threshold that distinguishes normal from anomalous behavior in dynamic settings. To address this, we introduce an active le…

Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
Jianzhen Chen, Zhijian Luo, Cheng Cheng, Jun Hou, Shaohua Zhang, Chenggang Shu
https://arxiv.org/abs/2507.07552

Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain statistically significant samples. To address this, this study employs a novel semi-supervised deep learning model, GC-SWGAN, aimed at identifying galaxy rings from high-resolution images of the DESI Legacy Imaging Surveys. We selected over 5,000 confirmed r…

WOCD: A Semi-Supervised Method for Overlapping Community Detection Using Weak Cliques
Shaozhen Ma, Hanchen Wang, Dong Wen, Wenjie Zhang, Wei Huang, Ying Zhang
https://arxiv.org/abs/2508.00927

WOCD: A Semi-Supervised Method for Overlapping Community Detection Using Weak Cliques
Overlapping community detection (OCD) is a fundamental graph data analysis task for extracting graph patterns. Traditional OCD methods can be broadly divided into node clustering and link clustering approaches, both of which rely solely on link information to identify overlapping communities. In recent years, deep learning-based methods have made significant advancements for this task. However, existing GNN-based approaches often face difficulties in effectively integrating link, attribute, and…

Unified Semi-Supervised Pipeline for Automatic Speech Recognition
Nune Tadevosyan, Nikolay Karpov, Andrei Andrusenko, Vitaly Lavrukhin, Ante Jukic
https://arxiv.org/abs/2506.07659

Unified Semi-Supervised Pipeline for Automatic Speech Recognition
Automatic Speech Recognition has been a longstanding research area, with substantial efforts dedicated to integrating semi-supervised learning due to the scarcity of labeled datasets. However, most prior work has focused on improving learning algorithms using existing datasets, without providing a complete public framework for large-scale semi-supervised training across new datasets or languages. In this work, we introduce a fully open-source semi-supervised training framework encompassing the …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- A Computational Theory and Semi-Supervised Algorithm for Clustering
Nassir Mohammad

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Haoran Xi, Chen Liu, Xiaolin Li
https://arxiv.org/abs/2508.06517

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Automated polyp segmentation is essential for early diagnosis of colorectal cancer, yet developing robust models remains challenging due to limited annotated data and significant performance degradation under domain shift. Although semi-supervised learning (SSL) reduces annotation requirements, existing methods rely on generic augmentations that ignore polyp-specific structural properties, resulting in poor generalization to new imaging centers and devices. To address this, we introduce Frequen…

Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations
Linh Nguyen, Marcel Boersma, Erman Acar
https://arxiv.org/abs/2507.01980

Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations
Fraudulent activity in the financial industry costs billions annually. Detecting fraud, therefore, is an essential yet technically challenging task that requires carefully analyzing large volumes of data. While machine learning (ML) approaches seem like a viable solution, applying them successfully is not so easy due to two main challenges: (1) the sparsely labeled data, which makes the training of such approaches challenging (with inherent labeling costs), and (2) lack of explainability for th…

Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum
Agastya Raj, Zehao Wang, Tingjun Chen, Daniel C Kilper, Marco Ruffini
https://arxiv.org/abs/2507.21728

Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum
Accurate modeling of the gain spectrum in Erbium-Doped Fiber Amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward multi-vendor solutions. In this work, we propose a generalized few-shot transfer learning architecture based on a Semi-Supervised Self-Normalizing Neural Network (SS-NN) that leverages internal EDFA features - such as VOA input or output power and attenuation, to improve gain spectrum prediction. Our SS-NN model employs a…

Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer
Dilina Perera, Samuel McAllister, Joan Josep Cerd\`a, Thomas Vogel
https://arxiv.org/abs/2506.20899

Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer
We use a semi-supervised, neural-network based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole-dipole and Lennard-Jones interactions. As input for the neural network we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang--Landau simulations. We demonstrate that by m…

Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models
Rihui Jin, Zheyu Xin, Xing Xie, Zuoyi Li, Guilin Qi, Yongrui Chen, Xinbang Dai, Tongtong Wu, Gholamreza Haffari
https://arxiv.org/abs/2506.06137

Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models
Table reasoning (TR) requires structured reasoning over semi-structured tabular data and remains challenging, particularly for small language models (SLMs, e.g., LLaMA-8B) due to their limited capacity compared to large LMs (LLMs, e.g., GPT-4o). To narrow this gap, we explore program-based TR (P-TR), which circumvents key limitations of text-based TR (T-TR), notably in numerical reasoning, by generating executable programs. However, applying P-TR to SLMs introduces two challenges: (i) vulnerabi…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/6]:
- Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach
Wenyun Li, Wenjie Huang, Chen Sun

AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Lei Zhu, Jun Zhou, Rick Siow Mong Goh, Yong Liu
https://arxiv.org/abs/2506.20563

AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Vision Transformer has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transformer requires a large amount of labeled data to be effective, which hinders its applicability in annotation scarce semi-supervised learning scenario where only limited labeled data is available. State-of-the-art semi-supervised learning methods propose combinatorial CNN-Transformer learning to cross teach a transform…

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Lin-Han Jia, Si-Yu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li
https://arxiv.org/abs/2508.07299

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Neuro-symbolic (Nesy) learning improves the target task performance of models by enabling them to satisfy knowledge, while semi/self-supervised learning (SSL) improves the target task performance by designing unsupervised pretext tasks for unlabeled data to make models satisfy corresponding assumptions. We extend the Nesy theory based on reliable knowledge to the scenario of unreliable knowledge (i.e., assumptions), thereby unifying the theoretical frameworks of SSL and Nesy. Through rigorous t…

Encoder-Inverter Framework for Seismic Acoustic Impedance Inversion
Junheng Peng, Yingtian Liu, Mingwei Wang, Yong Li, Wen Feng
https://arxiv.org/abs/2507.19933 https://

Encoder-Inverter Framework for Seismic Acoustic Impedance Inversion
Seismic acoustic impedance inversion is one of the most challenging tasks in geophysical exploration, facing difficulties such as insufficient well-logging data and non-linear relationships. Most inversion methods rely on strategies such as semi-supervised learning, and their inversion accuracy and stability still need further improvement. In this work, we propose a novel inversion framework that innovatively maps continuous seismic traces into high-dimensional linear features, transforming the…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning
Salar Nouri, Mojdeh Karbalaee Motalleb, Vahid Shah-Mansouri, Seyed Pooya Shariatpanahi

Efficient Learning for Product Attributes with Compact Multimodal Models
Mandar Kulkarni
https://arxiv.org/abs/2507.19679 https://arxiv.org/pdf/2507.19679

Efficient Learning for Product Attributes with Compact Multimodal Models
Image-based product attribute prediction in e-commerce is a crucial task with numerous applications. The supervised fine-tuning of Vision Language Models (VLMs) faces significant scale challenges due to the cost of manual or API based annotation. In this paper, we investigate label-efficient semi-supervised fine-tuning strategies for compact VLMs (2B-3B parameters) that leverage unlabeled product listings through Direct Preference Optimization (DPO). Beginning with a small, API-based, annotated…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/8]:
- Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Jungtaek Kim

AI for the Routine, Humans for the Complex: Accuracy-Driven Data Labelling with Mixed Integer Linear Programming
Mohammad Hossein Amini, Mehrdad Sabetzadeh, Shiva Nejati
https://arxiv.org/abs/2507.04990

AI for the Routine, Humans for the Complex: Accuracy-Driven Data Labelling with Mixed Integer Linear Programming
The scarcity of accurately labelled data remains a major challenge in deep learning (DL). Many DL approaches rely on semi-supervised methods, which focus on constructing large datasets that require only a minimal amount of human-labelled data. Since DL training algorithms can tolerate moderate label noise, it has generally been acceptable for the accuracy of labels in large training datasets to fall well short of a perfect 100%. However, when it comes to testing DL models, achieving high label …