arXiv:2404.11161
BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification
원문 초록
Pre-processing whole slide images (WSIs) can impact classification performance. Our study shows that using fixed hyper-parameters for pre-processing out-of-domain WSIs can significantly degrade performance. Therefore, it is critical to search domain-specific hyper-parameters during inference. However, searching for an optimal parameter set is time-consuming. To overcome this, we propose BAHOP, a novel Similarity-based Basin Hopping optimization for fast parameter tuning to enhance inference performance on out-of-domain data. The proposed BAHOP achieves 5\% to 30\% improvement in accuracy with $\times5$ times faster on average.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport
Fast Bayesian Optimization of Function Networks with Partial Evaluations
Amortized variational transdimensional inference
HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.