arXiv:2003.08505
공식 코드 · Papers with Code
A Metric Learning Reality Check
원문 초록
Deep metric learning papers from the past four years have consistently claimed great advances in accuracy, often more than doubling the performance of decade-old methods. In this paper, we take a closer look at the field to see if this is actually true. We find flaws in the experimental methodology of numerous metric learning papers, and show that the actual improvements over time have been marginal at best.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Why and How: Knowledge-Guided Learning for Cross-Spectral Image Patch Matching
AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking
Global Ground Metric Learning with Applications to scRNA data
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers
Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content
토론
이 논문과 연결된 포럼 글이 아직 없습니다.