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Visual Analytics Research for Improving Training Data Quality

♠ (citations > 200)

  • Max-margin majority voting for learning from crowds (NIPS 2015) pdf
  • Improving learning-from-crowds through expert validation (IJCAI 2017) pdf
  • ImageNet: A large-scale hierarchical image database (CVPR 2009) pdf
  • Limits on learning machine accuracy imposed by data quality (NIPS 1995) pdf
  • 大数据可用性的研究进展 (软件学报 2016) html
  • To err is human: Building a safer health system (NAP 2000) pdf
  • Pervasive label errors in test sets destabilize machine learning benchmarks (NeurIPS Datasets and Benchmarks 2021) pdf
  • Data warehousing special report: Data quality and the bottom line (ADT 2002) pdf
  • Data management: Still a major obstacle to AI success (2019) html
  • Towards better analysis of machine learning models: A visual analytics perspective (Visual Informatics 2017) html
  • Visual analytics in deep learning: An interrogative survey for the next frontiers (TVCG 2019) pdf
  • VIS4ML: An ontology for visual analytics assisted machine learning (TVCG 2019) pdf
  • Recent research advances on interactive machine learning (JOV 2019) html
  • The state of the art in enhancing trust in machine learning models with the use of visualizations (CGF 2020) pdf
  • State of the art of visual analytics for explainable deep learning (CGF 2023) pdf
  • “Everyone wants to do the model work, not the data work”: Data cascades in high-stakes AI (CHI 2021) pdf
  • Towards human-guided machine learning (IUI 2019) pdf
  • A survey of visual analytics techniques for machine learning (CVM 2021) pdf
  • 可视化与人工智能交叉研究综述 (中国科学 2021) pdf
  • Extending the nested model for user-centric XAI: A design study on GNN-based drug repurposing (TVCG 2023) pdf
  • Onelabeler: A flexible system for building data labeling tools (CHI 2022) pdf
  • Symphony: Composing interactive interfaces for machine learning (CHI 2022) pdf
  • Steering data quality with visual analytics: The complexity challenge (Visual Informatics 2018) pdf
  • C2A: Crowd consensus analytics for virtual colonoscopy (VAST 2016) pdf
  • CMed: Crowd analytics for medical imaging data (TVCG 2021) pdf
  • An interactive method to improve crowdsourced annotations (TVCG 2019) pdf
  • Interactive correction of mislabeled training data (VAST 2019) pdf
  • An approach to supporting incremental visual data classification (TVCG 2015) pdf
  • V‐Awake: A visual analytics approach for correcting sleep predictions from deep learning models (CGF 2019) pdf
  • Classifier‐guided visual correction of noisy labels for image classification tasks (CGF 2020) pdf
  • DETOXER: A visual debugging tool with multiscope explanations for temporal multilabel classification (CG&A 2022) pdf
  • LabelVizier: Interactive validation and relabeling for technical text annotations (arXiv 2023) pdf
  • VANT: A visual analytics system for refining parallel corpora in neural machine translation (PacificVis 2022) pdf
  • OoDAnalyzer: Interactive analysis of out-of-distribution samples (TVCG 2021) pdf
  • DeepLens: Interactive out-of-distribution data detection in NLP models (CHI 2023) pdf
  • Contrastive identification of covariate shift in image data (VIS 2021) pdf
  • Perspective: Leveraging human understanding for identifying and characterizing image atypicality (IUI 2023) pdf
  • HetVis: A visual analysis approach for identifying data heterogeneity in horizontal federated learning (TVCG 2023) pdf
  • DataPilot: Utilizing quality and usage information for subset selection during visual data preparation (CHI 2023) pdf
  • Analyzing the noise robustness of deep neural networks (VAST 2018) pdf
  • Analyzing the noise robustness of deep neural networks (TVCG 2021) pdf
  • VERB: Visualizing and interpreting bias mitigation techniques for word representations (arXiv 2021) pdf
  • Visual identification of problematic bias in large label spaces (arXiv 2022) pdf
  • Visual analysis of discrimination in machine learning (TVCG 2021) pdf
  • FairRankVis: A visual analytics framework for exploring algorithmic fairness in graph mining models (TVCG 2022) pdf
  • DECE: Decision explorer with counterfactual explanations for machine learning models (TVCG 2021) pdf
  • Visual Auditor: Interactive visualization for detection and summarization of model biases (VIS 2022) pdf
  • RMExplorer: A Visu-al Analytics Approach to Explore the Performance and the Fair-ness of Disease Risk Models on Population Subgroups (VIS 2022) pdf
  • LINGO: Visually debiasing natural language instructions to support task diversity (CGF 2023) pdf
  • D-BIAS: A causality-based human-in-the-loop system for tackling algorithmic bias (TVCG 2023) pdf
  • Parallel embeddings: A visualization technique for contrasting learned representations (IUI 2020) pdf
  • Explaining vulnerabilities to adversarial machine learning through visual analytics (TVCG 2020) pdf
  • Visual analytics of neuron vulnerability to adversarial attacks on convolutional neural networks (TiiS 2023) pdf
  • ConceptExplainer: Interactive explanation for deep neural networks from a concept perspective (TVCG 2023) pdf
  • DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation (EuroVis 2022) pdf
  • ESCAPE: Countering systematic errors from machine’s blind spots via interactive visual analysis (CHI 2023) pdf
  • VATLD: A visual analytics system to assess, understand and improve traffic light detection (TVCG 2021) pdf
  • Where can we help? A visual analytics approach to diagnosing and improving semantic segmentation of movable objects (TVCG 2022) pdf
  • A unified understanding of deep NLP models for text classification (TVCG 2022) pdf
  • ShortcutLens: A visual analytics approach for exploring shortcuts in natural language understanding dataset (TVCG 2023) pdf
  • ScatterShot: Interactive in-context example curation for text transformation (IUI 2023) pdf
  • SliceTeller: A data slice-driven approach for machine learning model validation (TVCG 2023) pdf
  • HardVis: Visual analytics to handle instance hardness using undersampling and oversampling techniques (CGF 2023) pdf
  • ConceptExplorer: Visual analysis of concept drifts in multi-source time-series data (VAST 2020) pdf
  • Diagnosing concept drift with visual analytics (VAST 2020) pdf
  • Angler: Helping machine translation practitioners prioritize model improvements (CHI 2023) pdf
  • Visual drift detection for event sequence data of business processes (TVCG 2022) pdf
  • Improving the usability of hierarchical representations for interactively labeling large image data sets (HCI 2011) pdf
  • Visual analytics for mobile eye tracking (TVCG 2017) pdf
  • VIAN: A visual annotation tool for film analysis (CGF 2019) pdf
  • VASSL: A visual analytics toolkit for social spambot labeling (TVCG 2020) pdf
  • IRVINE: A design study on analyzing correlation patterns of electrical engines (TVCG 2022) pdf
  • Spatial labeling: Leveraging spatial layout for improving label quality in non-expert image annotation (CHI 2021) pdf
  • Mediatable: Interactive categorization of multimedia collections (CG&A 2010) pdf
  • Director's cut: Analysis and annotation of soccer matches (CG&A 2016) pdf
  • Visual concept programming: A visual analytics approach to injecting human intelligence at scale (TVCG 2023) pdf
  • Comparing person-and process-centric strategies for obtaining quality data on amazon mechanical turk (CHI 2015) pdf
  • Revolt: Collaborative crowdsourcing for labeling machine learning datasets (CHI 2017) pdf
  • Rehumanized crowdsourcing: A labeling framework addressing bias and ethics in machine learning (CHI 2019) pdf
  • Crowdsourcing multi-label audio annotation tasks with citizen scientists (CHI 2019) pdf
  • Project sidewalk: A web-based crowdsourcing tool for collecting sidewalk accessibility data at scale (CHI 2019) pdf
  • Eliciting confidence for improving crowdsourced audio annotations (HCI 2022) pdf
  • Extracting references between text and charts via crowdsourcing (CHI 2014) pdf
  • Jury learning: Integrating dissenting voices into machine learning models (CHI 2022) pdf
  • Inter-active learning of ad-hoc classifiers for video visual analytics (VAST 2012) pdf
  • FDive: Learning relevance models using pattern-based similarity measures (VAST 2019) pdf
  • Visual classifier training for text document retrieval (TVCG 2012) pdf
  • Active learning and visual analytics for stance classification with ALVA (TiiS 2017) pdf
  • Analytic: An active learning system for trajectory classification (CG&A 2017) pdf
  • Aila: Attentive interactive labeling assistant for document classification through attention-based deep neural networks (CHI 2019) pdf
  • Peax: Interactive visual pattern search in sequential data using unsupervised deep representation learning (CGF 2020) pdf
  • Interactive learning for identifying relevant tweets to support real-time situational awareness (TVCG 2020) pdf
  • VIANA: Visual interactive annotation of argumentation (VAST 2019) pdf
  • Diagnosing ensemble few-shot classifiers (TVCG 2022) pdf
  • Towards visual explainable active learning for zero-shot classification (TVCG 2022) pdf
  • Comparing visual-interactive labeling with active learning: An experimental study (TVCG 2018) pdf
  • Towards user-centered active learning algorithms (CGF 2018) pdf
  • A visual analytics framework for explaining and diagnosing transfer learning processes (TVCG 2021) pdf
  • Designing interactive transfer learning tools for ML non-experts (CHI 2021) pdf
  • Polyphony: An interactive transfer learning framework for single-cell data analysis (TVCG 2023) pdf
  • Interactive graph construction for graph-based semi-supervised learning (TVCG 2021) pdf
  • Towards better caption supervision for object detection (TVCG 2022) pdf
  • Sparks of artificial general intelligence: Early experiments with gpt-4 (arXiv 2023) pdf
  • GPT-4 technical report (arXiv 2023) pdf
  • Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning (NIPS 2022) pdf
  • Rethinking the role of demonstrations: What makes in-context learning work? (EMNLP 2022) pdf
  • What makes good in-context examples for GPT-3? (DeeLIO 2022) pdf
  • Ground-truth labels matter: A deeper look into input-label demonstrations (EMNLP 2022) pdf
  • Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity (ACL 2022) pdf

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