Inspector Gadget: A Data Programming-based Labeling System for Industrial Images
Summary: Directly applying data programming to images without conversion for industrial labeling. Inspector Gadget fuses crowdsourcing, augmentation, and labeling functions to generate scalable weak labels for image classification, beating Snuba, GOGGLES, and self-learning CNN baselines without pretraining. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Geon Heo
- 2. Yuji Roh
- 3. Seonghyeon Hwang
- 4. Dayun Lee
- 5. Steven Euijong Whang
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,985 | TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications | 2023 | VLDB | 4.4156106e-05 |
| 10,465 | A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplaces | 2025 | SIGMOD | 4.1945683e-05 |
| 11,109 | SEER: An End-to-End Toolkit for Benchmarking Time Series Database Systems in Monitoring Applications | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 249 | Crowdsourced Databases: Query Processing with People | 2011 | CIDR | 0.00030740523 |
| 643 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018754451 |
| 1,215 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.0001323375 |
| 4,087 | Snorkel: Fast Training Set Generation for Information Extraction | 2017 | SIGMOD | 6.4607746e-05 |
| 4,451 | CLAMShell: Speeding up Crowds for Low-latency Data Labeling | 2016 | VLDB | 6.1738675e-05 |
| 5,251 | Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale | 2019 | SIGMOD | 5.6029615e-05 |
| 8,343 | CrowdGame: A Game-Based Crowdsourcing System for Cost-Effective Data Labeling | 2019 | SIGMOD | 4.5429217e-05 |
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