Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods
Abstract
Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is expensive to collect. To address it, many efforts have been made on training sophisticated models with few labeled data in an unsupervised and semi-supervised fashion. In this paper, we will review the recent progresses on these two major categories of methods. A wide spectrum of models will be categorized in a big picture, where we will show how they interplay with each other to motivate explorations of new ideas. We will review the principles of learning the transformation equivariant, disentangled, self-supervised and semi-supervised representations, all of which underpin the foundation of recent progresses. Many implementations of unsupervised and semi-supervised generative models have been developed on the basis of these criteria, greatly expanding the territory of existing autoencoders, generative adversarial nets (GANs) and other deep networks by exploring the distribution of unlabeled data for more powerful representations. We will discuss emerging topics by revealing the intrinsic connections between unsupervised and semi-supervised learning, and propose in future directions to bridge the algorithmic and theoretical gap between transformation equivariance for unsupervised learning and supervised invariance for supervised learning, and unify unsupervised pretraining and supervised finetuning. We will also provide a broader outlook of future directions to unify transformation and instance equivariances for representation learning, connect unsupervised and semi-supervised augmentations, and explore the role of the self-supervised regularization for many learning problems.
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For deep neural networks to achieve human-level performance, millions of labelled images are required for training.
“for deep neural networks to achieve human-level performance, millions of labelled images are required for training (Qi and Luo 2020).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Qi and Luo 2020
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“As discussed in previous sections, for deep neural networks to achieve human-level performance, millions of labelled images are required for training (Qi and Luo 2020).”
- Authors
- Qi, G.-J., & Luo, J.
- Affiliations
- Futurewei Technologies (United States) (US)
- University of Rochester (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-04-01
- Volume
- 44
- Issue
- 4
- Pages
- 2168-2187
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2922706279
- Cited by
- 315
- References
- 11
- Citations by year
- 2026: 28 · 2025: 63 · 2024: 63 · 2023: 70 · 2022: 45 · 2021: 28 · 2020: 14 · 2019: 4
- FWCI
- 15.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- representation learning, small labeled datasets, unsupervised learning, semi-supervised learning, disentangled representations, self-supervised representations, generative models, autoencoder, generative adversarial networks, unsupervised pre-training, supervised fine-tuning, data augmentation, invariance
- MeSH terms
- Supervised Machine Learning, Big Data, Algorithms, Neural Networks, Computer
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