Large-scale pre-trained models are now foundational in machine learning, but their standard pre-training objectives typically optimize for source-domain performance without considering what the model will be used for later. A new arXiv paper proposes a paradigm called "knowledge diversion" to address this gap, specifically for decomposable image generation models.
The abstract indicates that knowledge diversion is designed to align pre-training with downstream requirements, rather than treating pre-training as a generic, task-agnostic step. This marks a shift from conventional practice, where the downstream application is only considered after pre-training is complete.
As the source is a single abstract, details on how knowledge diversion works or its empirical results are not yet available. The paper's contribution, as described, is the conceptual reframing of pre-training as a process that should be guided by the intended end tasks.