Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods (1903.11260v2)

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.

Overview of "Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods"

This paper, authored by Guo-Jun Qi and Jiebo Luo, provides an extensive survey on the progress and challenges in unsupervised and semi-supervised learning, specifically within the context of limited labeled data, a scenario that frequently arises in real-world applications. The authors categorize numerous model architectures and learning strategies, mapping out how they relate and motivate explorations into new methodologies for addressing the small data challenges endemic in the big data era.

The document underscores the fact that the successes of deep neural networks typically hinge on sizable labeled datasets, which can be expensive to procure. Therefore, leveraging the vast amounts of available unlabeled data presents an enticing alternative, which has provoked significant interest in both unsupervised and semi-supervised learning approaches. Below is a more in-depth look at the discussed areas and implications of this survey.

Key Areas Discussed

1. Unsupervised Learning Methods:
   • Transformation Equivariant Representations (TER): The survey highlights methods for learning representations that equivary with input transformations. Techniques like Group-Equivariant Convolutions and Auto-Encoding Transformations are explored, which facilitate unsupervised training by maximizing the dependency between representations and transformations.

• Generative Models and Representation Disentanglement: Various architectures like Variational Auto-Encoders, GANs, and Flow-based models are discussed. The paper emphasizes the importance of learning disentangled representations, which provide interpretable generative factors critical for downstream tasks.
• Self-Supervised Learning: This section covers models that capitalize on self-supervisory signals derived inherently from the data. Autoregressive models, contrastive predictive coding, and other innovative approaches are mentioned for their potential to generate representations applicable to future tasks.

1. Semi-Supervised Learning Models:
   • Generative Models in Semi-Supervised Settings: Semi-supervised variants of VAE and GAN models are presented, showcasing how they can integrate labeled and unlabeled data.

• Teacher-Student Models: Several strategies are reviewed where predictions from models (teachers) guide the training of other models (students). Techniques like temporal ensembling and virtual adversarial training exhibit state-of-the-art performance in leveraging unlabeled data.

1. Domain Adaptation:
   • The survey categorizes domain adaptation into unsupervised and semi-supervised approaches, discussing how generative models can bridge the gap between source and target data domains.

Implications and Future Directions

• Bridging Equivariance and Invariance: A significant insight from the paper is the need to reconcile transformation equivariance in unsupervised learning with invariance desired in supervised settings. Theoretical advancements here could lead to more robust representations, reducing dependency on labeled data.
• Unsupervised vs. Supervised Pretraining: The survey suggests that unsupervised pretraining might offer better generalization to new tasks, mainly because it doesn't depend on specific pre-labeled datasets.
• Unifying Instance and Transformation Equivariance: By integrating methods that leverage both instance discrimination and transformation prediction, the authors anticipate advancing the performance of unsupervised models.
• Leveraging Self-Supervision: As a promising tool across various learning tasks, self-supervised learning serves as a regularizer, and its role in semi-supervised settings deserves deeper exploration for enhanced model stability and performance.

Conclusion

This paper offers a thorough and insightful overview of unsupervised and semi-supervised learning advancements in dealing with limited labeled data within the vast landscape of available unlabeled data. By dissecting existing methods and suggesting future avenues, Qi and Luo provide a robust framework that bridges current capabilities with prospective breakthroughs in artificial intelligence research. This survey is valuable not only for its comprehensive cataloging but also for its synthesis of principles that could guide future algorithmic and theoretical development in representation learning.