5.Contrastive Learning with Hard Negative Samples 论文标题:Contrastive Learning with Hard Negative Samples 论文方向:图像&文本领域,研究如何采样优质的难负样本 论文来源:ICLR2021 论文链接:https://arxiv.org/abs/2010.04592 论文代码:https://github.com/joshr17/HCL 对比学习在无监督表征学习领域的潜力无需多...
5.Contrastive Learning with Hard Negative Samples 论文标题:Contrastive Learning with Hard Negative Samples 论文方向:图像&文本领域,研究如何采样优质的难负样本 论文来源:ICLR2021 论文链接:https://arxiv.org/abs/2010.04592 论文代码:https://github.com/joshr17/HCL 对比学习在无监督表征学习领域的潜力无需多...
Loss: Finally, we rely on Noise-Contrastive Estimation for the loss function in similar ways that have been used for learning word embeddings in natural language models, allowing for the whole model to be trained end-to-end. 文章指出,优化contrastive loss其实是在优化互信息的一个下界 (lower boun...
我为大家整理了对比学习在最新各大顶会上的论文合集及相应代码,所列举的论文涉及领域包括但不限于CV, NLP, Audio, Video, Multimodal, Graph, Language model等,GitHub地址: coder-duibai/Contrastive-Learning-Papers-Codesgithub.com/coder-duibai/Contrastive-Learning-Papers-Codes (请大家多多点赞支持ヽ(✿...
这四种方式能够重复的利用已有的数据去生成更多的新的样本,从而完成对比学习的训练。这篇文章在训练上将mask language model (MLM),也就是transformer中常用的训练方式与contrastive learning loss结合起来,构成了一个整体的loss: 这样就能够充分利用好数据进行预训练。讲到文本数据的augmentation,其实可以研究一下一篇2019年...
2.ICLR2021对比学习(Contrastive Learning)NLP领域论文进展梳理 本篇文章则梳理了对比学习在ICLR2021、ICLR2020和NIPS2020中非常值得大家一读的一些经典论文,构思非常巧妙,涵盖了CV和NLP领域,且与之前两篇文章中介绍的模型均不重叠。后续等NIPS2021论文公开后,也会持续更新并分享给大家,话不多说,开始进...
(SCL)SUPERVISED CONTRASTIVE LEARNING FOR PRE-TRAINED LANGUAGE MODEL FINE-TUNING 使用了监督对比学习的思路,额外添加了一个loss,目的是使同一类样本尽可能离得近,不同类样本尽可能离得远。 denotes the normalized embedding of the final encoder hidden layer before the softmax projection...
1.对比学习(Contrastive Learning)在CV与NLP领域中的研究进展 2.ICLR2021对比学习(Contrastive Learning)NLP领域论文进展梳理 本篇文章则梳理了对比学习在ICLR2021、ICLR2020和NIPS2020中非常值得大家一读的一些经典论文,构思非常巧妙,涵盖了CV和NLP领域,且与之前两篇文章中介绍的模型均不重叠。后续等NIPS2021论文公开后...
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningBeliz GunelJingfei DuAlexis ConneauVeselin StoyanovInternational Conference on Learning Representations
We propose a novel objective for fine-tuning pre-trained language models that includes a supervised contrastive learning term that pushes examples from the same class close and examples of different classes further apart. The new term is similar to the contrastive objective used for self-supervised ...