企业网站建设排名资讯,百度pc端提升排名,叙述网站制作的流程,微信群二维码推广平台对比自监督学习技术是一种很有前途的方法#xff0c;它通过学习对使两种事物相似或不同的东西进行编码来构建表示。Contrastive learning有很多文章介绍#xff0c;区别于生成式的自监督方法#xff0c;如AutoEncoder通过重建输入信号获取中间表示#xff0c;Contrastive M… 对比自监督学习技术是一种很有前途的方法它通过学习对使两种事物相似或不同的东西进行编码来构建表示。Contrastive learning有很多文章介绍区别于生成式的自监督方法如AutoEncoder通过重建输入信号获取中间表示Contrastive Methods通过在特征空间建立度量学习判别不同类型的输入不需要重建信号而又充分挖掘了无标签数据之间的特征差异。 对比学习通过同时最大化同一图像的不同变换视图(例如剪裁翻转颜色变换等)之间的一致性以及最小化不同图像的变换视图之间的一致性来学习的。简单来说就是对比学习要做到相同的图像经过各类变换之后依然能识别出是同一张图像所以要最大化各类变换后图像的相似度因为都是同一个图像得到的。相反如果是不同的图像即使经过各种变换可能看起来会很类似就要最小化它们之间的相似度。通过这样的对比训练编码器(encoder)能学习到图像的更高层次的通用特征 (image-level representations)而不是图像级别的生成模型(pixel-level generation)。 本资源整理了最近几年特别是2020年对比无监督学习最新的一些必读论文方便需要的朋友研究使用。2020 •Contrastive Representation Learning: A Framework and Review, Phuc H. Le-Khac •Supervised Contrastive Learning, Prannay Khosla, 2020, [pytorch*] •A Simple Framework for Contrastive Learning of Visual Representations, Ting Chen, 2020, [pytroch, tensorflow*] •Improved Baselines with Momentum Contrastive Learning, Xinlei Chen, 2020, [tensorflow] •Contrastive Representation Distillation, Yonglong Tian, ICLR-2020 [pytorch*] •COBRA: Contrastive Bi-Modal Representation Algorithm, Vishaal Udandarao, 2020 •What makes for good views for contrastive learning, Yonglong Tian, 2020 •Prototypical Contrastive Learning of Unsupervised Representations, Junnan Li, 2020 •Contrastive Multi-View Representation Learning on Graphs, Kaveh Hassani, 2020 •DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations, John M. Giorgi, 2020 •On Mutual Information in Contrastive Learning for Visual Representations, Mike Wu, 2020 •Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition, Nakamasa Inoue, 20202019 •Momentum Contrast for Unsupervised Visual Representation Learning, Kaiming He, 2019, [pytorch] •Data-Efficient Image Recognition with Contrastive Predictive Coding, Olivier J. Hénaff, 2019 •Contrastive Multiview Coding, Yonglong Tian, 2019, [pytorch*] •Learning deep representations by mutual information estimation and maximization, R Devon Hjelm, ICLR-2019, [pytorch] •Contrastive Adaptation Network for Unsupervised Domain Adaptation, Guoliang Kang, CVPR-20192018 •Representation learning with contrastive predictive coding, Aaron van den Oord, 2018, [pytorch] •Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination, Zhirong Wu, CVPR-2018, [pytorch*] •Adversarial Contrastive Estimation, Avishek Joey Bose, ACL-2018,2017 •Time-Contrastive Networks: Self-Supervised Learning from Video, Pierre Sermanet, CVPR-2017 •Contrastive Learning for Image Captioning, Bo Dai, NeurIPS-2017, [lua*] Before 2017 •Noise-contrastive estimation for answer selection with deep neural networks, Jinfeng Rao, 2016, [torch] •Improved Deep Metric Learning with Multi-class N-pair Loss Objective, Kihyuk Sohn, NeurIPS-2016, [pytorch] •Learning word embeddings efficiently with noise-contrastive estimation, Andriy Mnih, NeurIPS-2013, •Noise-contrastive estimation: A new estimation principle for unnormalized statistical models, Michael Gutmann, AISTATS 2010, [pytorch] •Dimensionality Reduction by Learning an Invariant Mapping, Raia Hadsell, 2006往期精品内容推荐加州理工《数据驱动算法设计》课程(2020)视频及ppt分享神经网络经典书籍-《神经网络简要介绍》免费pdf分享深度强化学习圣经-《Reinforcement Learning-第二版》深度学习通信领域相关经典论文、数据集整理分享DeepMind深度学习系列讲座-10-深度学习里的表示学习知识图谱KG存储、可视化、公开数据集、图计算、图编程工具分享谷歌、微软、Facebook等2018最新面试题分享Geffery Hinton-数字代表模型从数据中抽取的知识、AI不会有寒冬Andrew Ng新课-《大众化AI》分享Tesla全自驾演示视频
