Targetsoften

Targetsoften is a coined term in machine learning referring to a family of techniques that soften or smooth the target signals used to train predictive models. In classification tasks, targetsoften typically entails replacing hard one-hot class labels with softened labels that allocate a small portion of probability mass to other classes, a practice similar to label smoothing. In regression or probabilistic forecasting, targetsoften may involve converting target values into probabilistic targets or incorporating uncertainty into the target by modeling it as a distribution rather than a single point estimate.

Mechanism: The softened targets can be derived from prior class frequencies, cross-validated predictions, or a teacher model in a distillation framework. The effect is to reduce overconfidence, improve calibration, and mitigate label noise.

Applications: Widely used to improve generalization on imbalanced or noisy datasets, in calibrated probabilistic forecasts, and in distillation-based training pipelines.

Implementation: Common approaches include explicit label smoothing with a smoothing factor alpha, Dirichlet-based target distributions, or soft targets produced by student-teacher models; software packages often provide options.

Limitations: May degrade performance if the smoothing is excessive or not aligned with true data distribution; requires tuning.

See also: Label smoothing; Knowledge distillation; Label noise; Probabilistic targets.