Dataset Condensation via Efficient Synthetic-Data Parameterization (2205.14959v2)

Abstract: The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. However, the existing approaches have fundamental limitations in optimization due to the limited representability of synthetic datasets without considering any data regularity characteristics. To this end, we propose a novel condensation framework that generates multiple synthetic data with a limited storage budget via efficient parameterization considering data regularity. We further analyze the shortcomings of the existing gradient matching-based condensation methods and develop an effective optimization technique for improving the condensation of training data information. We propose a unified algorithm that drastically improves the quality of condensed data against the current state-of-the-art on CIFAR-10, ImageNet, and Speech Commands.

• The paper introduces a condensation framework that leverages natural data regularities for efficient synthetic data generation.
• The multi-formation process maps smaller datasets onto larger synthetic sets while preserving inherent spatial and temporal patterns.
• Experimental results on CIFAR-10 and ImageNet demonstrate 10%-20% performance improvements with the proposed IDC algorithm.

Dataset Condensation via Efficient Synthetic-Data Parameterization

The paper "Dataset Condensation via Efficient Synthetic-Data Parameterization" addresses a critical concern in machine learning regarding the excessive computational and storage costs associated with training on massive datasets. The research proposes a novel framework for dataset condensation, aiming to synthesize a compact yet informative representation of large datasets, thereby alleviating the computational burden without significantly compromising performance.

Core Contributions

1. Novel Condensation Framework: The authors introduce a condensation framework that utilizes a unique parameterization method to generate synthetic datasets. This framework is informed by the regularity characteristics of natural data, which implies a lower-dimensional data subspace due to inherent statistical patterns. By leveraging these regularities, the framework is able to create multiple synthetic data points efficiently within a constrained storage budget.
2. Multi-Formation Process: A key innovation is the multi-formation process, which maps a smaller dataset configuration onto a larger synthetic dataset while preserving data regularity. This is achieved by using a deterministic operation that respects the natural orderliness in data, such as spatial regularities in images or temporal consistencies in audio, thus yielding regularized and larger synthetic datasets.
3. Theoretical Analysis and Algorithmic Development: The research provides theoretical analysis supporting the effectiveness of this multi-formation process, demonstrating conditions under which the proposed synthetic datasets can closely approximate the original dataset. An end-to-end optimization algorithm is designed, named Information-intensive Dataset Condensation (IDC), that significantly improves previous techniques by using a new gradient matching approach.
4. Experimental Validation: The synthetic datasets generated by the IDC algorithm outperformed existing state-of-the-art condensation and coreset selection methods across diverse datasets including CIFAR-10, ImageNet, and Speech Commands. For example, in CIFAR-10, the networks trained on the proposed synthetic data showed performance improvements of 10%-20% compared to existing state-of-the-art methods.

Implications and Future Directions

The implications of this research are manifold. Practically, the ability to condense datasets without severe losses in performance can lead to more efficient machine learning pipelines, especially beneficial in domains where computational resources are limited or where environmental costs are a concern. For instance, in continual learning scenarios, the proposed method demonstrates significant performance improvements over existing techniques by maintaining effective historical data representation which is crucial for maintaining performance over sequential tasks.

Theoretically, the work opens avenues for further exploration of synthetic data parameterization techniques. Future research could focus on refining the multi-formation functions or exploring alternative parameterization approaches perhaps using generative models that can learn to represent such synthetic data distributions more effectively.

Overall, the paper lays substantial groundwork for subsequent research in dataset condensation, effectively balancing storage constraints with the need for maintaining dataset representative capacity and computational efficiency. As the field advances, exploring hybrid approaches that combine parameterization with generative networks may yield further gains, offering a broader spectrum of possibilities for efficient model training methodologies.