Labelwithout

Labelwithout is a term used in data annotation and machine learning to describe data items in a dataset that lack explicit labels. It refers to instances that are deliberately left unlabeled or missing annotations, reflecting real-world collection scenarios where labeling is costly or impractical. The concept draws attention to the existence of data without standard coarse or fine-grained tags, rather than to a specific class or label type.

In practice, labelwithout data is often processed with methods that extract learning signals without relying on ground-truth labels. Self-supervised learning creates auxiliary tasks from the data itself to train representations, while weak or distant supervision uses indirect or noisy signals to guide model updates. Semi-supervised and active learning techniques may leverage unlabeled instances alongside a small labeled set to improve performance. Annotation tools may also provide a labelwithout workflow to collect contextual or metadata cues before final labeling.

Challenges include ambiguity in evaluation, risk of biased inference, and the need for careful validation. Advantages include reduced labeling costs, greater data utilization, and the potential to reveal structure not captured by existing labels. Labelwithout concepts intersect with unlabeled data, self-supervised learning, and weak supervision, and are often discussed in the context of data governance and annotation strategy.

See also: unlabeled data, self-supervised learning, semi-supervised learning, weak supervision.