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://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...
Such localization, however, depends upon the quality of image features often obtained using Contrastive learning frameworks. Most contrastive learning strategies opt for features to distinguish different classes. In the context of localization, however, there is no natural denition of classes. Therefore,...
soft contrastive learning, specifically designed to capture the multifaceted nature of state behaviors more accurately. The data augmentation process enriches the dataset with varied representations of normal states, while soft contrastive learning fine-tunes the model's sensitivity to the subtle differences...
Methods AddRemove Contrastive Learning
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...
Contrastive representation learning has proven to be an effective self-supervised learning method for images and videos. Most successful approaches are bas
softmax中的温度T的作用是调节输出的“平滑程度”。 很多和soft-label有关的应用都会用到温度T,知识蒸馏会用到T,contrastive learning里面温度T也是很重要的参数。不太一样的地方是在knowledge distillation里面…
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): ...
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。