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UNITI: Framework for multi-task learning across datasets to mitigate overfitting
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AI
DOI：10.1016/j.eswa.2025.127653.png)
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Abstract
En 中文
Multi-task learning (MTL) has emerged as a promising approach for improving generalization across related tasks by leveraging shared representations. However, existing MTL techniques primarily focus on intra-dataset learning, where tasks share a common dataset with multiple labels. This approach often fails to address real-world scenarios where tasks originate from heterogeneous datasets, leading to catastrophic forgetting and feature interference. To overcome these limitations, we propose UNITI (Unifying Neural with Inter-dataset for Task Integration), a novel inter-dataset multi-task learning framework that enables a single model to effectively learn from multiple distinct datasets. UNITI consists of two key components: (1) sequential dataset training, which reduces interference by updating model parameters systematically across datasets, and (2) feature-level knowledge distillation (KD), where a student model learns essential task-related features from dataset-specific teacher models. We validate UNITI using both CNN-based (ResNet50) and ViT-based (SHViT) architectures on facial recognition tasks (age estimation, emotion classification) and general object classification (Caltech-101). Experimental results show that UNITI achieves up to 5.17% improvement in accuracy over standard MTL methods and maintains comparable performance to single-task models while significantly reducing computational overhead. Notably, in emotion recognition, UNITI improved accuracy from 60.67% (single-task) to 65.84%, demonstrating its ability to preserve task-specific features in an interdataset setting. Our findings suggest that UNITI is a scalable and efficient alternative to traditional MTL approaches, with applications in real-world AI systems where diverse datasets must be integrated into a single model.
Keywords:
Multi-task learning (MTL)
Convolutional neural network (CNN)
Vision transformer (ViT)
Emotion recognition
Age recognition
Gender recognition
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
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