🏋️ Full Annotation

Full Annotation#

Labelling every example fully, the most costly supervision setting.

Important

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What it is#

Full annotation means labeling every example in a dataset with its ground-truth target — the complete, high-quality supervision that classic supervised learning assumes. Each image gets its boxes, each sentence its tags.

The cost#

It is manual, slow, and expensive — often the most tedious part of an ML project — and requires annotators following guidelines, whose disagreements become a data-quality issue (measured with inter-annotator agreement). At scale, labeling everything is simply infeasible.

Why it persists#

Despite the cost, fully annotated data gives the strongest signal and remains the go-to for production and the gold-standard benchmark. Its expense is exactly what motivates weak, semi-, and self-supervised learning, which trade some label quality for far less human effort.

Theme: Model Training & Optimization · All terminology

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Mind map — connected ideas

Weak Supervision · Label Noise · Computer Vision (CV) · Natural Language Processing (NLP) · Neural Networks · Manual review minutes

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