或者是:Soft Contrastive Learning for Time Series GitHub:https://github.com/seunghan96/softclt ICLR 2024的论文。 (从这篇开始,pretext task翻译为代理任务,更专业一点,也符合训练模型的辅助任务的意思.) 摘要 对比学习已被证明能有效地以自监督的方式从时间序列中学习表征。然而,对比相似的时间序列实例或时间序...
论文:Soft Contrastive Learning for Time Series 或者是:Soft Contrastive Learning for Time Series GitHub:https:///seunghan96/softclt ICLR 2024的论文。 (从这篇开始,pretext task翻译为代理任务,更专业一点,也符合训练模型的辅助任务的意思.) 摘要 对比学习已被证明能有效地以自监督的方式从时间序列中学习表征。
To address these limitations, we propose a soft contrastive learning method SCodeSearcher for code search, which highlights challenging examples by arranging high weights to them based on their challenging degrees in the contrastive learning objective. We conduct extensive experiments on five ...
在对比学习入门 A Primer on Contrastive Learning 中,我们介绍过对比学习,并指出对比学习就是一个动态的多分类问题。对比损失(contrastive loss)为: 其中f:Rn→Rm/Sm−1 是encoder函数,将样本映射到低维空间或低维球表面。记 (x,y) 是正样本对(positive pair), xi− 是从样本分布 pdata 中随机采样的样...
思路很简单,我更愿意称它为(soft self-)supervised contrastive learning,和之前G司的supervised contrastive learning比较类似,只不过标签是online生成的软标签 首先看一张图理解一下 具体实现层面,如图二,主要从FIFO q中挖掘KNN并计算与正样本的相关性,加入损失函数的计算中 公式也很简单 LxSNCLR=−1Nlog∑i...
Contrastive representation learning has proven to be an effective self-supervised learning method for images and videos. Most successful approaches are bas
这篇文章算是SimCSE的一个进阶版本吧,关于SimCSE的介绍之前我已经写了一篇小博客(文献阅读:SimCSE:Simple Contrastive Learning of Sentence Embeddings)介绍了一下了,这篇文章感觉像是基于SimCSE之后的一个优化版本。
对比学习 (Contrastive Learning): CLIP 使用对比学习的方式来训练模型。对比学习的目标是将语义上相似的图像和文本嵌入在一个共同的向量空间中,使得相似的图像和文本在这个空间中彼此接近,而不相似的图像和文本则相距较远。 多模态嵌入 (Multimodal Embedding): ...
{SCP}) model for few-shot sentiment analysis. First, we design a sentiment-aware chain of thought prompt module to guide the model to predict the sentiment from coarse grain to fine grain via a series of intermediate reasoning steps. Then, we propose a soft contrastive learning algorithm to ...
Inspired by emotional continuity, SCMM integrates soft contrastive learning with a new hybrid masking strategy to effectively mine the "short-term continuity" characteristics inherent in human emotions. During the self-supervised learning process, soft weights are assigned to sample pairs, enabling ...