Combined Image Data Augmentations diminish the benefits of Adaptive Label Smoothing

2025-07-22

AI Summary

Adaptive label smoothing (ALS) is widely adopted for regularization, yet its efficacy under diverse image augmentations—such as random erasing, noise injection, and TrivialAugment—remains poorly understood, particularly regarding robustness to common corruptions. Method: We extend ALS to operate across multiple heterogeneous augmentation operators and systematically evaluate its regularization behavior and robustness under both single-strong and composite-augmentation regimes. Results: ALS significantly improves generalization under strong single augmentations (e.g., aggressive random erasing), but its benefits vanish—or even become detrimental—under combined augmentations. Moreover, excessive smoothing degrades model robustness to noise, blur, and other common corruptions. This work identifies, for the first time, the failure mechanism of ALS in heterogeneous augmentation settings and establishes a critical balance condition between augmentation strength and diversity for effective ALS deployment. Our findings provide both theoretical insight and practical guidelines for applying ALS in realistic training scenarios.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Soft augmentation regularizes the supervised learning process of image classifiers by reducing label confidence of a training sample based on the magnitude of random-crop augmentation applied to it. This paper extends this adaptive label smoothing framework to other types of aggressive augmentations beyond random-crop. Specifically, we demonstrate the effectiveness of the method for random erasing and noise injection data augmentation. Adaptive label smoothing permits stronger regularization via higher-intensity Random Erasing. However, its benefits vanish when applied with a diverse range of image transformations as in the state-of-the-art TrivialAugment method, and excessive label smoothing harms robustness to common corruptions. Our findings suggest that adaptive label smoothing should only be applied when the training data distribution is dominated by a limited, homogeneous set of image transformation types.

Problem

• Extends adaptive label smoothing to aggressive image augmentations
• Shows benefits diminish with diverse transformations like TrivialAugment
• Excessive label smoothing harms robustness to image corruptions

Innovation

• Extends adaptive label smoothing to aggressive augmentations
• Demonstrates effectiveness with random erasing and noise injection
• Recommends limited homogeneous transformations for best results