Code available at https://github.com/mfederici/Multi-View-Information-Bottleneck 摘要:信息瓶颈原理为表示学习提供了一种信息论方法,通过训练编码器保留与预测标签相关的所有信息,同时最小化表示中其他多余信息的数量。然而,最初的公式需要标记数据来识别多余的信息。在这项工作中,我们将这种能力扩展到多视图无监督设置,其中提供
Chen T, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations[C]//International conference on machine learning. PMLR, 2020: 1597-1607. 1. 前言 本文作者为了了
转 计算机小白学习中查看完整翻译. 点击下载英文论文 Abstract Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data
预训练模型:A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is or use tran
论文笔记:SoundNet: Learning Sound Representations from Unlabeled Video SoundNet: Learning Sound Representations from Unlabeled Video Yusuf Aytar∗ Carl Vondrick∗ Antonio Torralba 2016 NIPS 这篇文章是顺着一维卷积相
ABSTRACT Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. 预训练自然语言表示的时候,增加模型的大小经常导致下游任务的表现提升。 However,at some point fur
BERT, or Bidirectional Encoder Representations from Transformers BERT是google最新提出的NLP预训练方法,在大型文本语料库(如维基百科)上训练通用的“语言理解”模型,然后将该模型用于我们关心的下游NLP任务(如分类、阅读理解)。 BERT优于以前的方法,因为它是用于预训练NLP的第一个**无监督,深度双向**系统。