Learning with not Enough Data Part 2: Active Learning
The performance of supervised learning tasks improves with more high-quality labels available. However, it is expensive to collect a large number of labeled samples. Active learning is one paradigm to deal with not enough labeled data, when there are resources for labeling more data samples but under a limited budget....
This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling work involved, but within a budget limit, and therefore we need to be smart when selecting which samples to label. Notations Symbol Meaning K Number of unique class labels. (xl,y)∼X,y∈{0,1}K Labeled dataset. y is a one-hot representation of the true label. u∼U Unlabeled dataset. D=X∪U The entire dataset, including both labeled and unlabeled examples. x Any sample which can be either labeled or unlabeled. xi The i-th sample. U(x)…
related reading
- Growing or Compressing Datasets · Introduction to Data-Centric AIdcai.csail.mit.edu
- Learning with not Enough Data Part 1: Semi-Supervised Learning | Lil'Loglilianweng.github.io
- Thinking about High-Quality Human Data | Lil'Loglilianweng.github.io
- The Little Book of Deep Learningfleuret.org
- Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities | PDFarxiv.org
- Semi-Supervised Learning: Techniques & Examples [2024]v7labs.com
- Why you need to improve your training data, and how to do it << Pete Warden's blogpetewarden.com
- A Course in Machine Learningciml.info
- MAI-Thinking-1: Building a Hill-Climbing Machinemicrosoft.ai
- Understanding Deep Learningudlbook.github.io
- Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study | Soft Computing and Intelligent Information Systemssci2s.ugr.es
- deeplearningbook.org/contents/ml.htmldeeplearningbook.org