A Survey of Human-in-the-loop for Machine Learning

Abstract

Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches. In this paper, we survey existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. Using the above categorization, we summarize major approaches in the field; along with their technical strengths/ weaknesses, we have simple classification and discussion in natural language processing, computer vision, and others. Besides, we provide some open challenges and opportunities. This survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.

References in corpus (35)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Transformers in Vision: A Survey
• Pre-trained Models for Natural Language Processing: A Survey
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• Domain Generalization: A Survey
• Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
• A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis
• EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
• Object Detection in 20 Years: A Survey
• Fine-Tuning Language Models from Human Preferences
• A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples
• The 2018 DAVIS Challenge on Video Object Segmentation
• Attentional Encoder Network for Targeted Sentiment Classification
• Efficient Transformers: A Survey
• Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network
• Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
• WhittleSearch: Interactive Image Search with Relative Attribute Feedback
• Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers
• Fast Video Crowd Counting with a Temporal Aware Network
• Dialogue Learning With Human-In-The-Loop
• Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
• Video Object Segmentation and Tracking: A Survey
• Semi-Automatic Data Annotation guided by Feature Space Projection
• Learning from Few Samples: A Survey
• Learning to summarize from human feedback
• Interactive Video Object Segmentation in the Wild
• Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem
• DeepCap: Monocular Human Performance Capture Using Weak Supervision
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• Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities
• Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI
• Generative Melody Composition with Human-in-the-Loop Bayesian Optimization
• Rise of the HaCRS: Augmenting Autonomous Cyber Reasoning Systems with Human Assistance
• When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis

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• A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors
• A Workflow Model for Holistic Data Management and Semantic Interoperability in Quantitative Archival Research
• DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
• Modyn: Data-Centric Machine Learning Pipeline Orchestration
• Peek into the `White-Box': A Field Study on Bystander Engagement with Urban Robot Uncertainty
• Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving
• Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy
• Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control
• Research evolution of metal organic frameworks: A scientometric approach with human-in-the-loop
• Continual Learning at the Edge: An Agnostic IIoT Architecture
• ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text
• From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0
• Online Behavior Modification for Expressive User Control of RL-Trained Robots