Modular Meta-Learning with Cross-Domain Knowledge Composition

Inspired by Wang et al.'s (2024) MoCL modular approach and Zheng et al.'s (2023) work on preventing zero-shot degradation, this research proposes a meta-learning system that learns composable modules across domains. Unlike current methods that either isolate parameters (MoCL) or preserve zero-shot ability (ZSCL), our approach would meta-learn: (1) domain-specific modules (e.g., "texture," "geometry," "temporal dynamics"), (2) cross-domain composition rules, and (3) module importance predictors for new tasks. This could enable zero-shot transfer to novel task combinations (e.g., applying fruit quality classification modules from Mishra et al. (2025) to medical image analysis) while maintaining continual learning capabilities. The innovation lies in meta-learning the composition space itself—something current modular and transfer methods don't address—potentially achieving the universal adaptation goals of SAMCL but with better interpretability and efficiency.

References:

1. Meta-Learning Based Continual Learning Methods for Fine-grained Fruit Quality Classification. Aayush Mishra, Prathamesh Gadekar, Parijat Deshpande, Manasi S. Patwardhan (2025). 2025 IEEE Applied Sensing Conference (APSCON).
2. Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual Learning. Bomin Wang, Xinzhe Luo, X. Zhuang (2024). International Conference on Medical Image Computing and Computer-Assisted Intervention.
3. Rehearsal-Free Modular and Compositional Continual Learning for Language Models. Mingyang Wang, Heike Adel, Lukas Lange, Jannik Strotgen, Hinrich Schütze (2024). North American Chapter of the Association for Computational Linguistics.
4. Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models. Zangwei Zheng, Mingyu Ma, Kai Wang, Ziheng Qin, Xiangyu Yue, Yang You (2023). IEEE International Conference on Computer Vision.