  In the era of big data, the sheer volume and complexity of datasets pose
significant challenges in machine learning, particularly in image processing
tasks. This paper introduces an innovative Autoencoder-based Dataset
Condensation Model backed by Koopman operator theory that effectively packs
large datasets into compact, information-rich representations. Inspired by the
predictive coding mechanisms of the human brain, our model leverages a novel
approach to encode and reconstruct data, maintaining essential features and
label distributions. The condensation process utilizes an autoencoder neural
network architecture, coupled with Optimal Transport theory and Wasserstein
distance, to minimize the distributional discrepancies between the original and
synthesized datasets. We present a two-stage implementation strategy: first,
condensing the large dataset into a smaller synthesized subset; second,
evaluating the synthesized data by training a classifier and comparing its
performance with a classifier trained on an equivalent subset of the original
data. Our experimental results demonstrate that the classifiers trained on
condensed data exhibit comparable performance to those trained on the original
datasets, thus affirming the efficacy of our condensation model. This work not
only contributes to the reduction of computational resources but also paves the
way for efficient data handling in constrained environments, marking a
significant step forward in data-efficient machine learning.
