KIND: Knowledge Integration and Diversion for Training Decomposable Models
This addresses deployment challenges for pre-trained models in resource-constrained scenarios, though it is incremental as it builds on existing pre-training and decomposition techniques.
The paper tackles the problem of deploying pre-trained models under resource constraints and domain shifts by proposing KIND, a method that constructs decomposable models with learngenes and tailors, enabling adaptive recombination and mitigating negative transfer, as demonstrated through extensive experiments.
Pre-trained models have become the preferred backbone due to the increasing complexity of model parameters. However, traditional pre-trained models often face deployment challenges due to their fixed sizes, and are prone to negative transfer when discrepancies arise between training tasks and target tasks. To address this, we propose KIND, a novel pre-training method designed to construct decomposable models. KIND integrates knowledge by incorporating Singular Value Decomposition (SVD) as a structural constraint, with each basic component represented as a combination of a column vector, singular value, and row vector from U, Σ, and V^\top matrices. These components are categorized into learngenes for encapsulating class-agnostic knowledge and tailors for capturing class-specific knowledge, with knowledge diversion facilitated by a class gate mechanism during training. Extensive experiments demonstrate that models pre-trained with KIND can be decomposed into learngenes and tailors, which can be adaptively recombined for diverse resource-constrained deployments. Moreover, for tasks with large domain shifts, transferring only learngenes with task-agnostic knowledge, when combined with randomly initialized tailors, effectively mitigates domain shifts. Code will be made available at https://github.com/Te4P0t/KIND.