SSL (4) 썸네일형 리스트형 [논문리뷰] Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty 논문 링크 :https://arxiv.org/abs/1906.12340https://ojs.aaai.org/index.php/AAAI/article/view/5966 Self-Supervised Learning for Generalizable Out-of-Distribution Detection | Proceedings of the AAAI Conference on Artifici ojs.aaai.org 최근 OOD(out-of-distribution) detection에 관심이 생겨서 이와 관련된 다양한 논문을 survey 중에 해당 논문을 발견했다. 해당 논문은 NIPS 2019 논문으로 Self-supervised learning을 accuracy관점이 아닌 모델의 Robustness 관점에서 바라.. [논문리뷰] A Simple Framework for Contrastive Learning of Visual Representations 논문 링크 : https://arxiv.org/abs/2002.05709 A Simple Framework for Contrastive Learning of Visual Representations This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to under arxiv.org 해당 논문은 MoCo와 같이 self.. [논문리뷰] Momentum Contrast for Unsupervised Visual Representation Learning 논문링크 : https://arxiv.org/abs/1911.05722 Momentum Contrast for Unsupervised Visual Representation Learning We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large a arxiv.org 해당 논문은 Facebook AI Resera.. [논문리뷰] FixMatch: Simplifiying Semi-Supervised Learning with Consistency and Confidence 논문 링크 : https://arxiv.org/abs/2001.07685 FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-label arxiv.org 해당 논문은 Semi.. 이전 1 다음
