Data augmentation instead of explicit regularization (1806.03852v5)

Abstract: Contrary to most machine learning models, modern deep artificial neural networks typically include multiple components that contribute to regularization. Despite the fact that some (explicit) regularization techniques, such as weight decay and dropout, require costly fine-tuning of sensitive hyperparameters, the interplay between them and other elements that provide implicit regularization is not well understood yet. Shedding light upon these interactions is key to efficiently using computational resources and may contribute to solving the puzzle of generalization in deep learning. Here, we first provide formal definitions of explicit and implicit regularization that help understand essential differences between techniques. Second, we contrast data augmentation with weight decay and dropout. Our results show that visual object categorization models trained with data augmentation alone achieve the same performance or higher than models trained also with weight decay and dropout, as is common practice. We conclude that the contribution on generalization of weight decay and dropout is not only superfluous when sufficient implicit regularization is provided, but also such techniques can dramatically deteriorate the performance if the hyperparameters are not carefully tuned for the architecture and data set. In contrast, data augmentation systematically provides large generalization gains and does not require hyperparameter re-tuning. In view of our results, we suggest to optimize neural networks without weight decay and dropout to save computational resources, hence carbon emissions, and focus more on data augmentation and other inductive biases to improve performance and robustness.

• The paper demonstrates that data augmentation can effectively replace explicit regularization, improving model generalization on benchmark datasets.
• It systematically compares implicit and explicit methods, showing that data augmentation sustains performance even with limited training data.
• The study underscores the benefits of reduced computational tuning and lower environmental impact, advocating a resource-efficient training shift.

Insights into Data Augmentation as a Regularization Technique

The paper "Data Augmentation Instead of Explicit Regularization" by Alex Hernandez-Garcia and Peter König presents an analytical exploration of regularization in deep learning models, particularly focusing on the role of data augmentation versus explicit techniques like weight decay and dropout. The research systematically examines the effectiveness and practicality of using data augmentation to enhance generalization in neural networks and whether it can serve as a viable alternative to conventional explicit regularization approaches.

The authors begin by delineating explicit versus implicit regularization, providing clarity to a field often muddled with ambiguity. Explicit regularization is defined as techniques that constrain the representational capacity of neural networks, such as weight decay and dropout. In contrast, implicit regularization does not explicitly limit capacity but influences the optimization process indirectly, with examples including batch normalization and data augmentation.

The core investigation revolves around whether data augmentation alone can achieve comparable or superior generalization to models trained with both explicit regularization and data augmentation. Through empirical analysis on benchmark datasets such as ImageNet, CIFAR-10, and CIFAR-100, the authors demonstrate that data augmentation consistently enhances model performance without the need for fine-tuning hyperparameters associated with explicit regularization. This is particularly significant given that hyperparameter tuning can be computationally expensive and time-consuming.

A salient aspect of the findings is the adaptability of data augmentation across varying dataset sizes and model architectures. As the quantity of training data is reduced, models incorporating data augmentation retain a larger fraction of their predictive performance compared to those relying on explicit regularization. This robustness in data-sparse scenarios marks a critical advantage, especially when data collection is costly or unfeasible.

Moreover, the paper addresses the environmental impact of model training. By reducing reliance on explicit regularization, which requires computationally intensive hyperparameter searches, data augmentation emerges as a resource-efficient alternative, aligning with contemporary concerns over the carbon footprint of AI technologies.

The theoretical underpinnings of the research draw from statistical learning theory, where increased sample sizes generally bolster generalization. Data augmentation effectively simulates this by generating diverse examples, thus providing analogous benefits without explicit sample increases. Furthermore, the research underscores the notion that data augmentation leverages domain knowledge, introducing perceptually meaningful transformations rather than generic noise, which is an explicit regularization method’s typical modus operandi.

From a practical perspective, this work suggests a paradigm shift in model training strategies by recommending prioritization of data augmentation over explicit regularizers. This shift not only simplifies the training regimen but also enhances the portability and scalability of models across different datasets and tasks. By presenting a compelling case through robust empirical results and theoretical insights, the authors challenge the entrenched dependency on explicit regularization in deep learning.

In conclusion, Hernandez-Garcia and König’s paper provides a comprehensive evaluation of data augmentation as a regularization strategy. It advocates for its role as a primary tool in improving model generalization, thereby proposing a potential rethinking of traditional training regimes in deep learning. The work sets a foundation for future studies to explore optimized data augmentation strategies and their extended implications across other domains in artificial intelligence.