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This chapter explores advanced techniques for training neural networks with little to no supervised information. In particular, this chapter covers transfer learning, semi-supervised learning, and self-supervised learning for learning useful features for downstream tasks. The chapter finishes by covering the basics of popular generative modeling frameworks—namely VAE, Normalizing Flow, and Generative Adversarial Networks.

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Transfer Learning and Semi-supervised Learning

  • 章节

  • 发表日期：

    2026年

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  • Yiran Chen,
  • Hai Li,
  • Huanrui Yang

摘要

This chapter explores advanced techniques for training neural networks with little to no supervised information. In particular, this chapter covers transfer learning, semi-supervised learning, and self-supervised learning for learning useful features for downstream tasks. The chapter finishes by covering the basics of popular generative modeling frameworks—namely VAE, Normalizing Flow, and Generative Adversarial Networks.