Unverified50% confidenceFactExact time
APRIL freezes the BEATs backbone as a shared feature extractor, avoiding representation drift caused by fine-tuning and greatly reducing trainable parameters.
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAPRIL is an exemplar-free domain-incremental learning framework based on a shared frozen BEATs backbone that sequentially inherits and isolates low-rank experts, so that adaptation to a new domain does not alter the old models.76% similarUnverified扩散模型的训练目标为简单的去噪均方误差，相比GAN避免了对抗训练的不稳定性，降低了模式崩塌的风险68% similarUnverifiedThe approach of freezing the main model and training only a lightweight anchor generator provides a cost-controllable path for practical deployment.67% similarUnverifiedRATED's contextual bandit module can rapidly adapt to runtime tool failures without requiring model retraining.66% similarUnverifiedDistScene's distillation approach alleviates the problem of scarce scene-level training data.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1079419API
curl https://kongchang.com/api/v1/knowledge/claims/1079419MCP
get_claim(id=1079419)