Knowledge Diversion: A Novel Paradigm for Pre-training Decomposable Image Generation Models
Large-scale pre-trained models have become foundational to modern machine learning, yet conventional pre-training objectives primarily optimize source-domain performance without explicitly considering downstream requirements. Consequently, transferable and task-specific knowledge remains entangled within a shared parameter space, limiting selective knowledge reuse and efficient downstream adaptation. This limitation is particularly consequential for image-generation models, which must accommodate diverse conditions and domains. To address this challenge, we introduce Knowledge Diversion (KDiv), a condition-aware pre-training paradigm for learning inherently decomposable generative models....
| Authors | Yucheng Xie, Fu Feng, Ruixiao Shi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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