Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Jebraeeli, Vahid"'
Limited data availability in machine learning significantly impacts performance and generalization. Traditional augmentation methods enhance moderately sufficient datasets. GANs struggle with convergence when generating diverse samples. Diffusion mod
Externí odkaz:
http://arxiv.org/abs/2406.17238
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 Ko
Externí odkaz:
http://arxiv.org/abs/2405.13866