Heterogeneous Transfer Learning

Heterogeneous transfer learning addresses the challenge of leveraging knowledge from a source dataset to improve model performance on a target dataset with differing feature spaces, data distributions, or label spaces. Current research focuses on developing robust methods, often employing deep neural networks like autoencoders and ResNet architectures, to effectively transfer knowledge across these heterogeneous domains, including techniques like step-wise fine-tuning to mitigate negative transfer. This approach is particularly valuable in applications with limited labeled data in the target domain, such as medical imaging and remote sensing, where it enables improved model accuracy and efficiency compared to training solely on the scarce target data. The resulting advancements have significant implications for various fields by enabling more effective use of diverse and often limited datasets.