A Comprehensive Survey of Dataset Distillation
IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 6, 2023
Summary
Dataset distillation synthesizes small datasets from large ones to improve deep learning efficiency. This review explores frameworks, challenges, and future directions for dataset distillation techniques.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Computer Science
Background:
- Deep learning's rapid advancement relies on massive data and computing power.
- Unlimited data growth challenges limited computational resources.
- Dataset distillation offers a solution by creating smaller, representative datasets.
Purpose of the Study:
- To provide a comprehensive overview of dataset distillation.
- To analyze existing distillation frameworks and algorithms.
- To identify limitations and future research avenues.
Main Methods:
- Categorizing dataset distillation into meta-learning and data matching frameworks.
- Exploring factorized dataset distillation approaches.
- Reviewing performance comparisons and applications.
Main Results:
- Dataset distillation effectively compresses large datasets.
- Current methods face limitations with high-resolution data and complex label spaces.
- The paper offers a holistic understanding of the field.
Conclusions:
- Dataset distillation is a promising technique for efficient data processing in deep learning.
- Further research is needed to address current limitations.
- The review highlights key challenges and future directions for promoting the field.
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