Title: One-for-All Model Initialization with Frequency-Domain Knowledge
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Model Initialization, Variable-Sized Models, Frequency Domain Knowledge Transfer

Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm, yet the knowledge of pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.
In response to this challenge, recent approaches typically resort to either parameter selection, which fails to capture the interdependent structure of this knowledge, or parameter prediction using generative models that depend on impractical access to large network collections. In this paper, we empirically demonstrate that a model's foundational, task-agnostic knowledge -- its "learngene" -- is encoded within the low-frequency components of its weights, and can be inherited efficiently by downstream models.
Based on this insight, we propose FRONT (FRequency dOmain kNowledge Transfer), a novel framework that uses the Discrete Cosine Transform (DCT) to isolate the low-frequency "learngene". This learngene can be seamlessly adapted to initialize models of arbitrary size via simple truncation or padding, a process that is entirely training-free. For enhanced performance, we propose an optional low-cost refinement process that introduces a spectral regularizer to further improve the learngene's transferability. Extensive experiments show that FRONT achieves the state-of-the-art performance, accelerates convergence by up to 15✖ in vision tasks, and reduces training FLOPs by an average of 40.5\% in language tasks.