hard negative sample困难负样本采样:≠负样本,,但算是负采样(negative sampleing)的一种特定类型。 Positive-Unlabeled learning:又称为Positive-Instance based Learning (PIL),通常用于处理在有限标记数据集中分类非常罕见的类别,或者构建二元分类器时面临未标记样本的情况。与标准的监督式学习任务不同,PU学习只提供关于...
1. ICLR 2021: Contrastive Learning with Hard Negative Samples Contrastive Learning with Hard Negative Samplesarxiv.org/abs/2010.04592 文章认为,好的难负样本有两项原则: 与原始样本的标签不同; 与原始样本尽量相似。 先明确一些term: q 为采样难负样本的分布; x+ 为positive sample,即与 x 同class,...
The notorious problem in contrastive learning, where large batch sizes and long training epochs are required to train the model, is intractable. Nevertheless, with the increment of the batch sizes, the proportion of false negatives in each batch also increases, leading to a diminishment of model ...
另外,除了指标方面,为了实际考察一下SNCSE是否真的可以对语义进行区分,文中还对negative sample以及soft negative sample的相似度分布进行了绘制,得到结果如下: 可以看到: 如果不使用BML,模型将缺乏辨别soft negative sample的能力,SimCSE一定程度上可以缓解上述问题,但是整体而言问题还是比较大的,但是加入了BML之后的SNCS...
Traditional MI classification relies on supervised learning; however, it faces challenges in acquiring large volumes of reliably labeled data and ensuring generalized high performance owing to specific experimental paradigms. To address these issues, this study proposes a contrastive self-supervised learning...
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval 密集检索 (DR) 的有效性通常需要与稀疏检索相结合 主要瓶颈在训练机制,训练中使用的负面实例不能代表不相关文档,如下图所示 本文介绍最邻近负对比估计(ANCE):从语料库的最邻近(ANN)索引构造负样本的计件制,该索引与学习...
We consider the question: how can you sample good negative examples for contrastive learning? We argue that, as with metric learning, learning contrastive representations benefits from hard negative samples (i.e., points that are difficult to distinguish from an anchor point). The key challenge to...
Guo-Jun Qi3† 1Peking University, 2Purdue University 3Laboratory for MAchine Perception and LEarning (MAPLE) http://maple-lab.net/ Abstract Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discrim- inate...
contrastive learning methods已经成为学习表征的最流行的自我监督方法之一。在计算机视觉中,对于目标检测和分割任务,无监督对比学习方法甚至优于有监督的预训练。 对比学习依赖于两个关键要素:similar (positive)(x, x+)和dissimilar (negative)(x, x−)pairs of data points. 相关方法的成功取决于正负对 的设计。
Robust Contrastive Learning Using Negative Samples with Diminished Semantics 使用语义减少的负样本进行鲁棒的对比学习 Abstract CNN倾向依赖于非语义的低级特征。这种依赖性被推测会导致模型对图像扰动或域位移缺乏鲁棒性。在本文中,我们证明了通过生成精心设计的负样本,对比学习可以学习更鲁棒的表示,并减少对这些特征的依...