Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank
Journal:
arXiv
Published Date:
Jun 1, 2024
Abstract
Local learning offers an alternative to traditional end-to-end
back-propagation in deep neural networks, significantly reducing GPU memory
usage. While local learning has shown promise in image classification tasks,
its application to other visual tasks remains limited. This limitation arises
primarily from two factors: 1) architectures tailored for classification are
often not transferable to other tasks, leading to a lack of reusability of
task-specific knowledge; 2) the absence of cross-scale feature communication
results in degraded performance in tasks such as object detection and
super-resolution. To address these challenges, we propose the Memory-augmented
Auxiliary Network (MAN), which introduces a simplified design principle and
incorporates a feature bank to enhance cross-task adaptability and
communication. This work represents the first successful application of local
learning methods beyond classification, demonstrating that MAN not only
conserves GPU memory but also achieves performance on par with end-to-end
approaches across multiple datasets for various visual tasks.
