What is Label Smoothing?
Label Smoothing is a regularization technique used in deep learning classification tasks to prevent overfitting and improve generalization. It works by smoothing the target labels, replacing hard one-hot encoded labels with a softer distribution that assigns some probability mass to other classes, thus encouraging the model to avoid overconfident predictions.
Why use Label Smoothing?
Label Smoothing can help improve the model’s performance by:
- Reducing overfitting: By smoothing the labels, the model is less likely to become overconfident in its predictions and memorize the training data.
- Improving generalization: Smoothing the labels can encourage the model to learn features that are more general and applicable to unseen data.
- Stabilizing training: Label Smoothing can help stabilize the training process by reducing the variance of the loss function.
Example of Label Smoothing in Python using Keras
import tensorflow as tf from tensorflow.keras.losses import CategoricalCrossentropy # Define the label smoothing parameter label_smoothing = 0.1 # Create the loss function with label smoothing loss_fn = CategoricalCrossentropy(label_smoothing=label_smoothing) # Compile the model with the smoothed loss model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy'])
Resources on Label Smoothing
- Label Smoothing in Deep Learning - an article explaining label smoothing and its benefits
- When Does Label Smoothing Help? - a research paper investigating the effectiveness of label smoothing
- Saturn Cloud - for free cloud compute