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Also known as: Soft Targets, Smoothed Labels
Target values represented as probability distributions over classes instead of one-hot vectors, improving stability and regularization.
DESCRIPTION
Soft labels encode uncertainty by distributing probability mass across classes, unlike hard one-hot targets. They arise from label smoothing or teacher model outputs in knowledge distillation. Benefits include better calibration, reduced overfitting, and improved generalization. Temperature scaling often softens teacher distributions to reveal ‘dark knowledge’. Careful tuning prevents underfitting caused by overly smooth targets.
ALTERNATIVE DEFINITIONS
Wikipedia
Label smoothing is a regularization technique that replaces the hard 0 and 1 targets with smoothed values.
Defines a principal method for generating soft labels via smoothing.
Geoffrey Hinton et al.
The ‘soft targets’ produced by the ensemble contain much more information than the hard targets and we show that this additional information is a crucial part of the transfer.
Introduces soft targets in knowledge distillation, emphasizing information beyond hard labels.
RELATED TERMS
Label Smoothing Knowledge Distillation Cross-Entropy Loss Calibration Temperature Scaling Probabilistic Classification Regularization Entropy
COMPETING TERMS
Hard Labels
KNOWLEDGE AREAS
AI Data Science MLOps Mathematics
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Label smoothing
- Keras - Label smoothing parameter in CCE
- PyTorch - Tutorial on distillation and soft targets
TOP QUESTIONS
Why use Soft Labels instead of hard labels?
They encode uncertainty, reduce overconfidence, and improve generalization, often yielding better calibration and robustness.
Category: Relevancy
How are Soft Labels produced?
Via label smoothing or knowledge distillation where teacher model probabilities serve as training targets.
Category: Overview
What is temperature in Soft Labels?
A scaling parameter that softens probability distributions, revealing more informative inter-class relationships.
Category: Example
What risks come with Soft Labels?
Excessive smoothing can underfit and blur class boundaries. Poorly calibrated teachers can propagate biases.
Category: Risks
How to choose label smoothing strength?
Tune the smoothing parameter on validation data; common values range from 0.05 to 0.2 for classification.
Category: HowTo
Do Soft Labels help with noisy labels?
Yes, they can mitigate noise by discouraging overconfident fits to erroneous hard labels.
Category: Opinion
Are Soft Labels compatible with cross-entropy?
Yes, cross-entropy naturally supports probabilistic targets, enabling straightforward training with soft labels.
Category: Requirements
Do Soft Labels improve calibration?
Often, yes. They reduce over-confident predictions and align probabilities more closely with outcomes.
Category: Opinion
Where to begin with Soft Labels?
Start by enabling label smoothing in your framework or training with teacher logits using a moderate temperature.
Category: HowTo