What is the difference between supervised, unsupervised, and self-supervised learning?

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Here’s a concise breakdown of the differences between supervised, unsupervised, and self-supervised learning:

1. Supervised Learning

• Definition: The model learns from a labeled dataset, where each input has a corresponding correct output (label).

• Goal: Predict the correct output for new, unseen inputs.

• Examples: Classification (spam detection), regression (house price prediction).

• How it works: The algorithm minimizes the difference between its predictions and the known labels by learning a mapping function.

• Data needed: Large amounts of labeled data, which can be costly to obtain.

2. Unsupervised Learning

• Definition: The model learns from unlabeled data, finding patterns or structures without explicit guidance.

• Goal: Discover hidden structures like clusters or reduce data dimensions.

• Examples: Clustering (customer segmentation), dimensionality reduction (PCA).

• How it works: It identifies similarities or distributions in the data but does not predict specific outputs.

• Data needed: Only input data, no labels required.

3. Self-Supervised Learning

• Definition: A hybrid approach where the model generates its own labels from the input data, learning to predict part of the data from other parts.

• Goal: Learn useful data representations without manual labeling.

• Examples: Predicting missing words in sentences (BERT), predicting future video frames.

• How it works: Creates pretext tasks where the input itself provides supervision, enabling the model to learn features transferable to downstream tasks.

• Data needed: Unlabeled data but leverages internal data structure to create supervisory signals.

Summary:

• Supervised uses explicit labels.

• Unsupervised uses no labels, focusing on structure discovery.

• Self-supervised creates labels from the data itself to learn representations efficiently.

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