A Survey on Mixup Augmentations and Beyond (2409.05202v2)

Abstract: As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive labeled data are unavailable. Among existing augmentations, Mixup and relevant data-mixing methods that convexly combine selected samples and the corresponding labels are widely adopted because they yield high performances by generating data-dependent virtual data while easily migrating to various domains. This survey presents a comprehensive review of foundational mixup methods and their applications. We first elaborate on the training pipeline with mixup augmentations as a unified framework containing modules. A reformulated framework could contain various mixup methods and give intuitive operational procedures. Then, we systematically investigate the applications of mixup augmentations on vision downstream tasks, various data modalities, and some analysis & theorems of mixup. Meanwhile, we conclude the current status and limitations of mixup research and point out further work for effective and efficient mixup augmentations. This survey can provide researchers with the current state of the art in mixup methods and provide some insights and guidance roles in the mixup arena. An online project with this survey is available at https://github.com/Westlake-AI/Awesome-Mixup.

• The paper introduces a unified framework that systematizes Mixup strategies across various modalities to enhance model robustness and generalization.
• The paper reviews experimental findings showing that Mixup variants outperform traditional augmentation methods on benchmarks like CIFAR and ImageNet.
• The paper highlights future challenges and cross-domain applications, urging further research to overcome issues such as manifold intrusion.

A Survey on Mixup Augmentations and Beyond

In recent years, Deep Neural Networks (DNNs) have significantly advanced various fields like image classification, object detection, and natural language processing, owing to their exceptional prowess in feature representation. A critical challenge in these areas concerns the availability of massive amounts of labeled data necessary for training these data-hungry models. Data Augmentation (DA) techniques, particularly Mixup augmentations, emerge as a promising solution by reducing overfitting through the synthesis of virtual training samples.

This paper provides a comprehensive survey on Mixup augmentations, highlighting their integration into a wide range of applications. Mixup fundamentally combines two or more samples through linear interpolation, allowing for the creation of intermediate representations self-sufficiently while offering significant generalization benefits. This paper explores the foundational Mixup methods and intricate details of their operational pipelines, offering a unifying framework encompassing various Mixup strategies.

Key Contributions

1. Unified Framework: The paper conceptualizes Mixup methods into a unified framework with two primary strategies based on Sample and Label Mixup Policies, further subdivided to match various training paradigms across different modalities, such as vision, language, graphs, and speech.
2. Systematic Review: A detailed breakdown of strategies is provided, showcasing Static Linear, Feature-based, Cutting-based, among other methods within supervised learning. In self-supervised and semi-supervised learning, Mixup enhances model robustness through the generation of synthetic samples, enabling efficient exploration across unknown data distributions. This survey plots a detailed picture of Mixup’s current methods and historical evolutions.
3. Cross-Domain Applicability: Besides vision tasks, the paper explores Mixup’s applications in domains like audio, text, graphs, and even molecular biology. Each modality benefits from tailored Mixup strategies, extending its utility beyond conventional image-based datasets.
4. Theoretical Insight and Challenges: Through analyzing models' intrinsic complexities and mixup’s role in improving calibration, robustness, and generalization, the authors point out open problems like Manifold Intrusion and propose various challenges for future inquiry.

Experimental Results and Analysis

• The paper reviews experimental findings on Mixup's effectiveness within conventional datasets such as CIFAR, ImageNet, and others, demonstrating notable improvements in generalization and model robustness.
• On classification benchmarks, Mixup variants like AutoMix, SAMix, and RecursiveMix significantly enhanced model performance compared to traditional augmentation methods.
• In tasks such as regression and segmentation, Mixup helps mitigate biases inherent in data distributions by facilitating smoother feature spaces and better discriminative models.

Implications and Forward-Looking Perspectives

1. Applicability to Multimodal Learning: The survey emphasizes Mixup’s potential in multimodal contexts, encouraging exploration into tasks where audio, text, and vision interplay to achieve superior integrative learning frameworks.
2. Leveraging Generative Models: The paper suggests utilizing advanced generative models like GANs and diffusion models to create high-quality synthetic data for Mixup to further intensify its application strengths in domains requiring creative sample constructions.
3. Unified Mixup Framework for Broad Applications: Establishing a standardized, adaptable Mixup framework could bridge domain-specific gaps, fostering a broader acceptance and application across heterogeneous machine learning tasks.

In conclusion, Mixup augmentation techniques present versatile tools for enhancing DNN training, particularly under constraints of limited labeled data. Although significant strides have been made, ongoing research is crucial for addressing existing limitations and expanding Mixup’s reach into broader machine learning applications. This survey serves as a valuable resource for researchers, guiding future directions and innovations in the continued development of Mixup methodologies.