A Second-Order Perspective on Model Compositionality and Incremental Learning
Angelo Porrello Lorenzo Bonicelli Pietro Buzzega Monica Millunzi Simone Calderara Rita Cucchiara
The fine-tuning of deep pre-trained models has revealed compositional properties, with multiple specialized modules that can be arbitrarily composed into a single, multi-task model. However, identifying the conditions that promote compositionality remains an open issue, with recent efforts concentrating mainly on linearized networks. We conduct a theoretical study that attempts to demystify compositionality in standard non-linear networks through the second-order Taylor approximation of the loss function. The proposed formulation highlights the importance of staying within the pre-training basin to achieve composable modules. Moreover, it provides the basis for two dual incremental training algorithms: the one from the perspective of multiple models trained individually, while the other aims to optimize the composed model as a whole. We probe their application in incremental classification tasks and highlight some valuable skills. In fact, the pool of incrementally learned modules not only supports the creation of an effective multi-task model but also enables unlearning and specialization in certain tasks. Code available at <https://github.com/aimagelab/mammoth>
Continual Learning, Model Compositionality, Ensemble Learning, Task Arithmetic
We explore compositionality in fine-tuning non-linear deep models, revealing that staying within the pre-training basin is key to creating effective incremental learners.