Expanding Small-Scale Datasets with Guided Imagination (2211.13976v6)

Abstract: The power of DNNs relies heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often expensive and time-consuming. To address this issue, we explore a new task, termed dataset expansion, aimed at expanding a ready-to-use small dataset by automatically creating new labeled samples. To this end, we present a Guided Imagination Framework (GIF) that leverages cutting-edge generative models like DALL-E2 and Stable Diffusion (SD) to "imagine" and create informative new data from the input seed data. Specifically, GIF conducts data imagination by optimizing the latent features of the seed data in the semantically meaningful space of the prior model, resulting in the creation of photo-realistic images with new content. To guide the imagination towards creating informative samples for model training, we introduce two key criteria, i.e., class-maintained information boosting and sample diversity promotion. These criteria are verified to be essential for effective dataset expansion: GIF-SD obtains 13.5% higher model accuracy on natural image datasets than unguided expansion with SD. With these essential criteria, GIF successfully expands small datasets in various scenarios, boosting model accuracy by 36.9% on average over six natural image datasets and by 13.5% on average over three medical datasets. The source code is available at https://github.com/Vanint/DatasetExpansion.

• The paper introduces a novel Guided Imagination Framework (GIF) that generates semantically enriched samples from small datasets, achieving up to 36.9% accuracy improvement.
• The proposed method combines class-maintained information boosting with sample diversity promotion, outperforming conventional data augmentation techniques.
• The cost-effective and scalable framework is applicable across domains, including healthcare, and paves the way for improved model generalization.

Expanding Small-Scale Datasets with Guided Imagination: A Review

The research paper titled "Expanding Small-Scale Datasets with Guided Imagination" addresses a critical challenge in the application of deep neural networks (DNNs): the scarcity of large-scale annotated datasets. The authors propose a novel methodology termed the Guided Imagination Framework (GIF) to autonomously expand small datasets by crafting new labeled samples using advanced generative models like DALL-E2 and Stable Diffusion.

Key Contributions

1. Dataset Expansion Task: The paper introduces the dataset expansion task, which aims to increase both the size and informational content of small-scale datasets. This task diverges from conventional data augmentation, which mainly focuses on predefined transformations without generating fundamentally new content.
2. Guided Imagination Framework (GIF): The GIF utilizes generative models to optimize latent features in a semantically meaningful space. It leverages two key criteria:
   • Class-Maintained Information Boosting: Ensures the imagined data remains consistent with the seed data class while introducing new information.
   • Sample Diversity Promotion: Encourages the creation of diverse content, thus enhancing model generalization.
3. Empirical Findings: The authors report significant performance improvements using their framework. Specifically, GIF-SD increased model accuracy by 36.9% on average across various natural image datasets and by 13.5% on average across different medical datasets, outperforming unguided expansions and traditional augmentations.
4. Theoretical Insights: The paper provides a theoretical framework suggesting that increasing dataset diversity through guided imagination can enhance model generalization, as demonstrated through $\delta$-cover and $\delta$-diversity assessments.
5. Efficiency and Applicability: The GIF offers a cost-effective means of dataset expansion, requiring less time and resources compared to manual data collection. It also supports different network architectures, making it adaptable across various hardware constraints and domains, including medical imaging.

Methodology and Experiments

The methodological backbone of the GIF involves synthesizing images through a series of controlled perturbations in the latent space guided by pre-trained models' priors. These perturbations are fine-tuned using CLIP's zero-shot capabilities to maintain class consistency while boosting the information entropy and sample diversity.

The experiments encompass several small-scale image datasets, demonstrating the framework's capability to generalize across diverse data types. The comparative analysis with state-of-the-art data augmentation techniques and zero-shot model applications further legitimizes the GIF's efficacy.

Implications and Future Directions

The implications of this research are twofold. Practically, it offers a scalable solution to dataset scarcity, enabling a broader application of DNNs with limited data availability, particularly in resource-constrained domains such as healthcare. Theoretically, it opens new avenues in algorithmic data generation, inviting further exploration into model-guided data synthesis techniques that might even surpass real data in terms of informativeness and diversity.

The research also hints at potential future developments, including the adaptation of GIF to other AI tasks like object detection and segmentation, beyond the current focus on classification. Additionally, the approach could be extended to explore more diverse generative models and inversion techniques to maximize dataset expansion benefits.

In conclusion, the paper makes a substantial contribution to AI research by providing a robust framework that effectively tackles the longstanding challenge of dataset expansion, steering the community towards more generalized and semi-automatic data generation methodologies.