A Comparative Study of Multi-Task Learning Approaches on Disjoint Datasets Ayri sik Veri Setlerinde ok G revli grenme Yakla simlarinin Kar sila stirilmasi

Date

2025-01-01

Author

Yasin, Ahmed Hani
Akagündüz, Erdem
Demir, Ibrahim

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Multi-task learning is an approach that aims to use resources more efficiently during inference by concurrently learning multiple tasks through a single model. This method seeks to improve model generalization and performance by leveraging shared feature extraction, rather than using separate models for different tasks. However, when working with disjoint datasets, completing the missing labels for each task becomes a costly and time-consuming process. In this study, we compare the simultaneous utilization of independent datasets and different multi-task learning methods by adding a classification task to an unsupervised trained segmentation model. Our proposed approach offers a scalable solution by preserving the original labeling structure of the datasets and eliminating the need for multi-label annotation.

Subject Keywords

Classification, Disjoint Dataset, Multi-Task Learning, Vision Transformer

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015455548&origin=inward
https://hdl.handle.net/11511/115904

Collections

Graduate School of Informatics, Conference / Seminar