Effective Data Augmentation With Diffusion Models (2302.07944v2)

Abstract: Data augmentation is one of the most prevalent tools in deep learning, underpinning many recent advances, including those from classification, generative models, and representation learning. The standard approach to data augmentation combines simple transformations like rotations and flips to generate new images from existing ones. However, these new images lack diversity along key semantic axes present in the data. Current augmentations cannot alter the high-level semantic attributes, such as animal species present in a scene, to enhance the diversity of data. We address the lack of diversity in data augmentation with image-to-image transformations parameterized by pre-trained text-to-image diffusion models. Our method edits images to change their semantics using an off-the-shelf diffusion model, and generalizes to novel visual concepts from a few labelled examples. We evaluate our approach on few-shot image classification tasks, and on a real-world weed recognition task, and observe an improvement in accuracy in tested domains.

Effective Data Augmentation With Diffusion Models

The paper authored by Trabucco et al., "Effective Data Augmentation With Diffusion Models," introduces a novel approach to data augmentation, leveraging pre-trained text-to-image diffusion models to enhance semantic diversity in datasets used for image classification tasks. This methodology aims to address the inherent limitations of traditional augmentation techniques, such as rotations and flips, which often fail to encapsulate the high-level semantic variations crucial for effective model training.

Overview

The authors propose DA-Fusion, a sophisticated data augmentation framework that applies text-to-image diffusion models to edit and diversify the training data beyond the capabilities of standard augmentation practices. DA-Fusion employs an off-the-shelf diffusion model to parameterize image-to-image transformations, facilitating semantic alterations while maintaining structural consistency aligned with the original data's inherent invariances. The method adapts to novel visual concepts using only a limited number of labeled images, thereby enhancing generalization capacities in few-shot learning scenarios.

Methodology

1. Diffusion Model Integration: The diffusion models used are pre-trained to translate text prompts into synthetic yet photorealistic imagery. By manipulating pre-trained models, DA-Fusion applies textual inversion to introduce adaptations necessary for handling visual concepts not present in the original generative model's training data. This adaptation involves fine-tuning new textual embeddings within the model's architecture.
2. Leakage Prevention: Recognizing potential evaluation biases, the authors employ two leakage prevention strategies—a model-centric approach that edits model weights and a data-centric strategy that obfuscates class information in prompts. This ensures synthesized data cannot inadvertently exploit knowledge from unseen classes ingrained in the pre-trained diffusion models.
3. SDEdit Application: The framework synthesizes data by integrating SDEdit, a method for guiding diffusion processes with real images as reference points. This allows synthetic data generation with varying degrees of similarity to real data, a diversity controlled through stochastic or fixed parameters.
4. Balancing Synthetic and Real Data: The balance between synthetic and real data is carefully managed using probabilistic sampling strategies. By treating augmentation intensity as a controllable hyperparameter, the framework fosters higher semantic diversity relative to existing methods.

Experimental Results

DA-Fusion has been tested extensively across various datasets, including PascalVOC, COCO, and a bespoke weed recognition dataset that is beyond the conventional vocabulary of diffusion models. The results indicate that DA-Fusion outperforms traditional augmentation techniques such as Real Guidance and baseline methods, achieving up to a 10% improvement in classification accuracy in certain few-shot domains. The method demonstrates robustness to variations in training data balance and hyperparameters, validating its out-of-the-box applicability.

Implications and Future Directions

The implications of this work are both practical and theoretical. Practically, DA-Fusion presents significant potential for enhancing image classification accuracy in contexts with limited labeled samples, thereby reducing the dependency on large, annotated datasets. Theoretically, it advances the understanding of how diffusion models can be integrated into data preprocessing pipelines to better capture complex semantic relationships within visual datasets.

Future research could address the integration of additional control mechanisms within diffusion models to fine-tune the extent and nature of augmentations. Moreover, expanding DA-Fusion's application to other domains such as video data and reinforcement learning-based decision-making will further showcase its versatility and efficacy.

This work stands as an exemplar of how emergent generative model capabilities can be operationalized in practical machine learning tasks, enriching the toolkit available for handling data scarcity and diversity in model training pipelines. The authors also underscore the importance of curating unbiased training datasets and developing bias mitigation strategies in light of the ethical considerations surrounding generative model use.