The Impact of Label Smoothing on Selective Classification Performance

Grunnleggende konsepter

Label smoothing negatively affects selective classification by exacerbating overconfidence and underconfidence, degrading model performance.

Sammendrag

The content explores the impact of label smoothing on selective classification performance. It reveals that label smoothing leads to a consistent degradation in selective classification despite improving accuracy. The analysis focuses on logit-level gradients, explaining how label smoothing affects overconfidence and underconfidence. Additionally, the effectiveness of logit normalization in recovering degraded performance caused by label smoothing is highlighted. Experimental details, results, and related work are discussed comprehensively.
Introduction

Label smoothing (LS) is a popular regularization method for deep neural networks.
LS negatively impacts selective classification (SC) by exacerbating overconfidence and underconfidence.
Logit normalization is effective in mitigating the degradation caused by LS.
Preliminaries

LS redistributes probability mass to improve classification accuracy.
SC aims to reject misclassifications based on predictive uncertainty.
MSP is used as a measure of uncertainty for SC.
Experimental Details

Evaluation conducted on ImageNet and Cityscapes datasets.
Training models from scratch with varying levels of label smoothing (LS).
Logit normalization applied to recover lost SC performance caused by LS.
Results

LS consistently degrades SC performance across different architectures and tasks.
Overconfidence and underconfidence are exacerbated by LS, impacting model's ability to reject misclassifications.
Logit normalization effectively improves SC performance for LS-trained models.
Related Work

Comparison with prediction with rejection methods like out-of-distribution detection.
Discussion on Mixup techniques and their impact on softening training targets.
Exploration of label smoothing beyond selective classification scenarios.

Statistikk

LS leads to noticeable degradation in SC performance despite improving accuracy.
LS weakens the MSP score's ability to differentiate between correct vs incorrect predictions.

Sitater

"Label smoothing increasingly regularizes the max logit as the true probability of error decreases."
"Logit normalization effectively reverses the imbalanced regularisation caused by label smoothing."

Viktige innsikter hentet fra

Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It

by Guoxuan Xia,... klokken arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14715.pdf

Dypere Spørsmål

How does label smoothing affect other aspects of model training beyond selective classification

Label smoothing can have effects beyond selective classification in model training. One significant impact is on model calibration, where the aim is to align a model's output probabilities with its empirical accuracy. Label smoothing encourages models to be less confident, which can improve calibration by reducing overconfidence. Additionally, label smoothing can affect the generalization of the model by preventing overfitting on the training labels. This regularization technique helps in improving test accuracy and stabilizing the training process by making the model more robust to noisy or incorrect labels.

What are potential drawbacks or limitations of using logit normalization for recovering from label smoothing effects

While logit normalization has shown effectiveness in recovering from label smoothing effects, there are potential drawbacks and limitations to consider. One limitation is that logit normalization may introduce additional hyperparameters that need tuning, such as determining the appropriate value for p in Eq. (17). Choosing an inappropriate value for p could lead to suboptimal results or even worsen performance. Another drawback is that logit normalization may not always provide consistent improvements across different models or datasets. The effectiveness of logit normalization could vary depending on factors like dataset characteristics, architecture complexity, and training objectives.

How can insights from this study be applied to improve other areas of machine learning research

The insights from this study can be applied to improve various areas of machine learning research beyond selective classification and label smoothing effects:

Model Calibration: Understanding how label smoothing affects confidence estimation can lead to improved calibration techniques.
Uncertainty Estimation: Insights into how label smoothing impacts uncertainty estimates can enhance methods for estimating aleatoric and epistemic uncertainties.
Regularization Techniques: The analysis of gradient behavior with label smoothing provides valuable information for developing new regularization methods that balance between accuracy improvement and uncertainty estimation.
Transfer Learning: Applying similar analyses to transfer learning scenarios could help optimize pre-trained models' performance when fine-tuned on specific tasks or domains.
5Out-of-Distribution Detection: Leveraging findings about overconfidence due to LS could aid in developing better OOD detection methods based on uncertainty estimates.

These applications demonstrate how understanding the nuances of label smoothing effects can contribute significantly across various machine learning research domains towards building more reliable and robust models with improved performance metrics such as accuracy, calibration, and uncertainty estimation capabilities."