What is Dataset Distillation Learning? A Comprehensive Overview

The key findings regarding the substitution of real data with distilled data are as follows:

1. Effectiveness as a Replacement: Distilled data is effective in retaining high task performance by compressing information related to the early training dynamics of models trained on real data. However, it should not be treated as a direct substitute for real data outside the typical evaluation setting of dataset distillation.

2. Information Content: Distilled data captures information analogous to what is learned from real data early in the training process2. This is evidenced by strong parallels in predictions between models trained on distilled data and those trained on real data with early stopping5.

3. Semantic Information: Individual distilled data points contain meaningful semantic information. Distilled images can influence real images semantically consistently, indicating that distilled data points encapsulate specific, recognizable semantic attributes.

4. Unique Nature: Distilled data behaves like real data at inference time, but it is highly sensitive to the training procedure. Adding real data to distilled data during training can sometimes improve and sometimes decrease model accuracy, underscoring the unique nature of distilled data.

Dataset distillation differs from traditional data compression methods in its approach to reducing the size of datasets. Traditional data compression methods typically select a limited number of representative data points from the original dataset, which often results in a loss of information and reduced effectiveness in training models. On the other hand, dataset distillation synthesizes a new set of data points that can effectively replace the original dataset for training purposes. This process creates a compact, synthetic dataset that encapsulates the essential information of the larger dataset, enabling efficient and effective model training. Unlike traditional compression methods, dataset distillation retains a higher degree of information content and utility, making it a more effective solution for addressing the challenges posed by the ever-growing size of datasets in machine learning.