What is Self-Supervised Learning in Deep Learning?

Self-Supervised Learning trains models using automatically generated labels derived from the data itself, reducing dependence on human annotation.

Traditional supervised learning requires massive labeled datasets, which are expensive and time-consuming to create. Self-Supervised Learning (SSL) addresses this limitation by generating supervision signals directly from raw data.

The model learns useful representations through pretext tasks.

Examples:

For NLP:

For vision:

SSL training pipeline:

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