Label Smoothing

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