Self-Supervised Learning
Self-Supervised Learning is a training paradigm where the model generates its own labels directly from the input data (e.g. masking words and predicting them), allowing training on massive unlabeled datasets without human labeling.
Frequently Asked Questions
How does self-supervised learning compare to supervised learning?▼
Supervised learning requires human annotators to label images or text. Self-supervised learning automates labeling by hiding parts of the input data and forcing the model to guess them.
Is LLM pre-training self-supervised?▼
Yes, because the next-word prediction objective automatically uses the subsequent words in the dataset as the labels.
Quick Facts
- CategoryModel Training
- Key ApplicationFoundation model pre-training, BERT training cycles, and visual representations
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