Informative Sample Mining Network

2000년 도입 · 논문 1편에서 사용

Informative Sample Mining Network is a multi-stage sample training scheme for GANs to reduce sample hardness while preserving sample informativeness. Adversarial Importance Weighting is proposed to select informative samples and assign them greater weight. The authors also propose Multi-hop Sample Training to avoid the potential problems in model training caused by sample mining. Based on the principle of divide-and-conquer, the authors produce target images by multiple hops, which means the image translation is decomposed into several separated steps.

출처: Informative Sample Mining Network for Multi-Domain Image-to-Image Translation

소개 논문: Informative Sample Mining Network for Multi-Domain Image-to-Image Translation