Semi-supervised Learning

AntTech

Jul 11, 2024 · Information Security

Enhancing Fraud Transaction Detection via Unlabeled Suspicious Records (GIANTESS Framework)

The paper presents GIANTESS, a novel semi‑supervised fraud detection framework that leverages online‑identified suspicious transactions to augment the feature space, generating pseudo‑labels for out‑of‑distribution samples and employing a hybrid loss to improve detection of covert fraudulent activities, achieving notable recall gains on real‑world datasets.

GIANTESSSemi-supervised Learningfraud detection

0 likes · 6 min read

Enhancing Fraud Transaction Detection via Unlabeled Suspicious Records (GIANTESS Framework)

HomeTech

Jul 7, 2023 · Artificial Intelligence

Multi-Modal Video Understanding and AIGC Video Generation at Autohome

This article presents a comprehensive multi-modal video understanding system for AIGC video generation, detailing technical architecture, GCN-based semi-supervised learning, and practical applications across automotive content scenarios.

AIGCBERTNeXtVLAD

0 likes · 8 min read

Multi-Modal Video Understanding and AIGC Video Generation at Autohome

Baidu Tech Salon

Apr 7, 2023 · Artificial Intelligence

Ambiguity-Resistant Semi-supervised Learning (ARSL) for Single-stage Object Detection

ARSL, an ambiguity‑resistant semi‑supervised learning framework for single‑stage object detection, introduces Joint‑Confidence Estimation and Task‑Separation Assignment to resolve selection and assignment ambiguities in pseudo‑labels, thereby markedly improving pseudo‑label quality and achieving state‑of‑the‑art AP gains on COCO benchmarks.

ARSLSemi-supervised Learningcomputer vision

0 likes · 8 min read

Ambiguity-Resistant Semi-supervised Learning (ARSL) for Single-stage Object Detection

Baidu Geek Talk

Mar 16, 2023 · Artificial Intelligence

PaddleDetection v2.6 Release: PP-YOLOE Family Expansion and Advanced Detection Algorithms

PaddleDetection v2.6 expands the PP‑YOLOE family with rotating, small‑object, dense‑object, and ultra‑lightweight edge‑GPU models, upgrades PP‑Human and PP‑Vehicle toolboxes, releases semi‑supervised, few‑shot and distillation learning methods, adds numerous state‑of‑the‑art algorithms, and improves infrastructure with Python 3.10, EMA filtering and AdamW support.

BaiduDeep LearningPP-YOLOE

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PaddleDetection v2.6 Release: PP-YOLOE Family Expansion and Advanced Detection Algorithms

DataFunSummit

Dec 23, 2022 · Artificial Intelligence

Data‑Centric AI Practices for Content Moderation at NetEase Yidun

The article presents NetEase Yidun’s data‑centric AI approach to content moderation, covering the background of Data‑Centric AI, the specific business and data challenges of content safety, comprehensive data pipelines—including collection, labeling, augmentation, selection, cleaning, iteration and testing—and the role of self‑, semi‑ and weak‑supervised learning in enhancing algorithm performance.

Algorithm InnovationData-Centric AISemi-supervised Learning

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Data‑Centric AI Practices for Content Moderation at NetEase Yidun

AntTech

Dec 19, 2022 · Artificial Intelligence

TransVCL: Attention‑Enhanced Video Copy Localization Network with Flexible Supervision

TransVCL introduces an end‑to‑end attention‑enhanced video copy localization network that leverages a custom Transformer, correlation‑Softmax similarity matrix, and temporal alignment module, combined with a semi‑supervised learning framework, achieving state‑of‑the‑art performance on VCSL and VCDB benchmarks.

AIAttentionSemi-supervised Learning

0 likes · 13 min read

TransVCL: Attention‑Enhanced Video Copy Localization Network with Flexible Supervision

AntTech

Nov 6, 2022 · Artificial Intelligence

Advanced Rule Learning, Constraint‑Adaptive Frameworks, and Semi‑Supervised Data Augmentation for Fraud Detection and Imbalanced Ranking

This article surveys recent Ant Group research on explainable fraud detection, including constraint‑adaptive rule‑set learning (CRSL), meta‑path guided rule generation (MetaRule), biased sampling for imbalanced ranking, and a semi‑supervised data‑augmentation framework (SDAT) for tabular data, highlighting their motivations, methodologies, deployments, and experimental results.

AI researchGraph Neural NetworksSemi-supervised Learning

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Advanced Rule Learning, Constraint‑Adaptive Frameworks, and Semi‑Supervised Data Augmentation for Fraud Detection and Imbalanced Ranking

DataFunTalk

Oct 30, 2022 · Artificial Intelligence

SPACE and Proton: Semi‑Supervised Knowledge Injection and Probing‑Tuning for Pretrained Conversational AI Models

This article reviews Alibaba DAMO‑ConvAI’s work on large‑scale conversational AI, comparing pretrained language and dialogue models, introducing the SPACE semi‑supervised knowledge‑injection framework and the Proton probing‑tuning method for extracting and applying model knowledge to downstream tasks.

Knowledge InjectionPretrained Dialogue ModelProbing Tuning

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SPACE and Proton: Semi‑Supervised Knowledge Injection and Probing‑Tuning for Pretrained Conversational AI Models

58 Tech

Sep 29, 2022 · Artificial Intelligence

End-to-End Speech Recognition Optimization and Deployment at 58.com

58.com’s AI Lab presents a comprehensive overview of its end‑to‑end speech recognition system, detailing data collection, semi‑supervised training, Efficient Conformer architecture, model compression, and deployment strategies that together achieve high accuracy across diverse acoustic conditions and large‑scale production workloads.

AIDeploymentEfficient Conformer

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End-to-End Speech Recognition Optimization and Deployment at 58.com

HomeTech

Sep 8, 2022 · Artificial Intelligence

Concept Tag Mining for Recommendation Systems: Methods, Challenges, and Solutions

This article presents a comprehensive overview of concept tag mining for recommendation systems, describing unsupervised pattern‑matching, semi‑supervised AutoPhase, and supervised NER approaches, analyzing their advantages and drawbacks, and offering practical solutions to tag duplication and quality issues.

NERNLPRecommendation systems

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Concept Tag Mining for Recommendation Systems: Methods, Challenges, and Solutions

DataFunSummit

Apr 17, 2022 · Artificial Intelligence

Precise Marketing Algorithms and Practices at Hello Mobility

This article presents Hello Mobility’s precise marketing system, covering its business background, value, framework, algorithmic capabilities such as Pu‑Learning LookAlike modeling, TSA semi‑supervised learning, and Graph Embedding, as well as identified pain points, project impact, and future directions for scaling and automation.

AIPrecise MarketingSemi-supervised Learning

0 likes · 12 min read

Precise Marketing Algorithms and Practices at Hello Mobility

Beike Product & Technology

Jan 7, 2022 · Artificial Intelligence

Beike Real Estate NLP Team Wins First Place in CCIR Cup 2021 Intelligent Human‑Computer Interaction Track

The Beike Real Estate NLP team secured first place in the CCIR Cup 2021 Intelligent Human‑Computer Interaction track by applying semi‑supervised and transfer learning techniques to small‑sample intent recognition and slot filling, and also presented the large‑scale Mandarin dialect speech benchmark KeSpeech at NeurIPS 2021.

AI competitionBERTNLP

0 likes · 5 min read

Beike Real Estate NLP Team Wins First Place in CCIR Cup 2021 Intelligent Human‑Computer Interaction Track

DataFunTalk

Aug 13, 2021 · Artificial Intelligence

Predictions for Speech Recognition Technology Over the Next Decade: Research and Application Directions

The article, authored by a former Stanford PhD now at Zoom, forecasts that by 2030 speech recognition will rely heavily on semi‑supervised learning, on‑device models, richer representations, and personalization, while applications such as transcription services and voice assistants will evolve modestly.

AIFuture TrendsSemi-supervised Learning

0 likes · 7 min read

Predictions for Speech Recognition Technology Over the Next Decade: Research and Application Directions

DataFunTalk

May 9, 2021 · Artificial Intelligence

Few-Shot Learning, Data Augmentation, and Multi‑Task Learning for Safety Modeling in Ride‑Hailing Platforms

This article presents Didi's exploration of few‑shot learning, data‑augmentation, semi‑supervised self‑training and multi‑task learning techniques to address the scarcity of labeled samples in safety and governance scenarios, demonstrating practical solutions and performance gains across various risk‑detection tasks.

AISemi-supervised Learningdata augmentation

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Few-Shot Learning, Data Augmentation, and Multi‑Task Learning for Safety Modeling in Ride‑Hailing Platforms

Didi Tech

Apr 20, 2021 · Artificial Intelligence

Few-Shot Learning, Data Augmentation, and Semi‑Supervised Methods for Improving Safety and Governance Models at Didi

To overcome scarce labeled data for safety and governance, Didi combines few‑shot learning with systematic data augmentation, self‑training semi‑supervised labeling, and multi‑task neural architectures, cutting labeling costs and reducing log‑loss by over 20% while boosting ROC‑AUC and PR‑AUC across harassment detection, expense‑complaint, and route‑intercept use cases.

AI SafetyDidiSemi-supervised Learning

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Few-Shot Learning, Data Augmentation, and Semi‑Supervised Methods for Improving Safety and Governance Models at Didi

Youku Technology

Apr 8, 2021 · Artificial Intelligence

Champion Solution of Media AI Alibaba Entertainment Video Object Segmentation Challenge

The Youku AI team won the Media AI Alibaba Entertainment Video Object Segmentation Challenge by enhancing the STM model with a spatial‑constrained memory reader, ASPP‑HRNet refinement, ResNeSt‑101 backbone, and a multi‑stage training pipeline, while also devising an unsupervised framework that combines DetectoRS detection, HRNet mask refinement, STM‑based association, and key‑frame optimization to achieve 95.5% test score on a large, richly annotated video dataset.

Deep LearningSemi-supervised LearningSpatial Memory Networks

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Champion Solution of Media AI Alibaba Entertainment Video Object Segmentation Challenge

JD Tech Talk

Dec 3, 2020 · Artificial Intelligence

Graph Algorithms and Graph Neural Networks for Fraud Detection

The article explains how building account relationship graphs and applying both traditional graph algorithms and modern graph neural networks—through community detection, anomaly detection, semi‑supervised and unsupervised learning, and dynamic graph techniques—can effectively identify and dismantle fraud groups in online services.

AIGraph Neural NetworksSemi-supervised Learning

0 likes · 11 min read

Graph Algorithms and Graph Neural Networks for Fraud Detection

Didi Tech

Nov 5, 2020 · Artificial Intelligence

Self-Learning Platform for Speech Recognition Model Optimization at DiDi

DiDi’s self‑learning ASR platform lets non‑technical users upload business data, automatically train, test and deploy models with semi‑supervised learning, hot‑word updates and LSTM rescoring, creating a closed‑loop pipeline that boosted vehicle voice‑interaction accuracy from around 80 % to over 95 % within months.

AISemi-supervised LearningSpeech Recognition

0 likes · 14 min read

Self-Learning Platform for Speech Recognition Model Optimization at DiDi

DataFunTalk

Jun 28, 2020 · Artificial Intelligence

Applying UDA Semi‑Supervised Learning to Financial Text Classification: Experiments and Insights

This article investigates the practical performance of Google’s 2019 Unsupervised Data Augmentation (UDA) framework on real‑world financial text classification tasks, detailing experiments with limited labeled data, domain‑out‑of‑distribution samples, noisy labels, and comparisons between BERT and lightweight TextCNN models.

BERTSemi-supervised LearningText Classification

0 likes · 21 min read

Applying UDA Semi‑Supervised Learning to Financial Text Classification: Experiments and Insights

DataFunTalk

Nov 27, 2019 · Artificial Intelligence

Front‑Fusion Based Recognition Pipeline for High‑Precision Map Static Obstacle Detection

This article presents a comprehensive front‑fusion recognition pipeline for high‑definition map static obstacle detection, detailing depth‑aware mapping, precise multi‑sensor calibration, point‑cloud registration, and semi‑supervised learning techniques that improve detection accuracy over traditional image‑only methods.

AIHD mapLiDAR

0 likes · 11 min read

Front‑Fusion Based Recognition Pipeline for High‑Precision Map Static Obstacle Detection