Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasets

Autor:	Lê, Hoàng-Ân, Pham, Minh-Tan
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Partial multi-task learning where training examples are annotated for one of the target tasks is a promising idea in remote sensing as it allows combining datasets annotated for different tasks and predicting more tasks with fewer network parameters. The na\"ive approach to partial multi-task learning is sub-optimal due to the lack of all-task annotations for learning joint representations. This paper proposes using knowledge distillation to replace the need of ground truths for the alternate task and enhance the performance of such approach. Experiments conducted on the public ISPRS 2D Semantic Labeling Contest dataset show the effectiveness of the proposed idea on partial multi-task learning for semantic tasks including object detection and semantic segmentation in aerial images. Comment: Accepted for oral presentation at IGARSS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.15394 Zobrazit plný text záznamu View this record from Arxiv