# Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation

arXiv: https://arxiv.org/abs/2111.10932 · HTML: https://mlsanity.com/paper/2111.10932/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [Semi-Supervised Learning with Generative Adversarial Networks](https://mlsanity.com/paper/1606.01583/) (2016)
- [There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average](https://mlsanity.com/paper/1806.05594/) (2018)
- [Reducing Label Effort: Self-Supervised meets Active Learning](https://mlsanity.com/paper/2108.11458/) (2021)
- [Deep Metric Transfer for Label Propagation with Limited Annotated Data](https://mlsanity.com/paper/1812.08781/) (2018)
- [Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets](https://mlsanity.com/paper/2104.12690/) (2021)

## Built on by (most cited first, 7 of 7)

- [VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON](https://mlsanity.com/paper/2306.07890/) (2023)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [Revisiting Active Learning in the Era of Vision Foundation Models](https://mlsanity.com/paper/2401.14555/) (2024)
- [Active Self-Semi-Supervised Learning for Few Labeled Samples](https://mlsanity.com/paper/2203.04560/) (2022)
- [fairDMS: Rapid Model Training by Data and Model Reuse](https://mlsanity.com/paper/2204.09805/) (2022)
- [Augmentation-aware Self-supervised Learning with Conditioned Projector](https://mlsanity.com/paper/2306.06082/) (2023)
- [Joint Debiased Representation and Image Clustering Learning with Self-Supervision](https://mlsanity.com/paper/2209.06941/) (2022)

## Abstract (arXiv)

As the adoption of deep learning techniques in industrial applications grows with increasing speed and scale, successful deployment of deep learning models often hinges on the availability, volume, and quality of annotated data. In this paper, we tackle the problems of efficient data labeling and annotation verification under the human-in-the-loop setting. We showcase that the latest advancements in the field of self-supervised visual representation learning can lead to tools and methods that benefit the curation and engineering of natural image datasets, reducing annotation cost and increasing annotation quality. We propose a unifying framework by leveraging self-supervised semi-supervised learning and use it to construct workflows for data labeling and annotation verification tasks. We demonstrate the effectiveness of our workflows over existing methodologies. On active learning task, our method achieves 97.0% Top-1 Accuracy on CIFAR10 with 0.1% annotated data, and 83.9% Top-1 Accuracy on CIFAR100 with 10% annotated data. When learning with 50% of wrong labels, our method achieves 97.4% Top-1 Accuracy on CIFAR10 and 85.5% Top-1 Accuracy on CIFAR100.