Deep Neural Network Parameter Selection via Dataset Similarity Under Meta-Learning Framework

Optimizing the performance of deep neural networks (DNNs) remains a significant challenge due to the sensitivity of models to both hyperparameter selection and weight initialization. Existing approaches typically address these two factors independently, which often leads to limiting adaptability and overall effectiveness. In this paper, we present a novel meta-learning framework that jointly recommends hyperparameters and initial weights by leveraging dataset similarity. Our method begins by extracting meta-features from a collection of historical datasets. For a given query dataset, similarity is computed based on distances in the meta-feature space, and the most similar historical datasets are used to recommend the underlying parameter configurations. To capture the diverse characteristics of image datasets, we introduce two complementary types of meta-features. The first, referred to as shallow or visible meta-features, comprises five groups of statistical measures that summarize color and texture information. The second, termed deep or invisible meta-features, consists of 512 descriptors extracted from a convolutional neural network pre-trained on ImageNet. We evaluated our framework in 105 real-world image classification tasks, using 75 datasets for historical modeling and 30 for querying. Experimental results with both vision transformers and convolutional neural networks demonstrate that our approach consistently outperforms state-of-the-art baselines, underscoring the effectiveness of dataset-driven parameter recommendation in deep learning.