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...
In order to solve the above problems, this paper proposes a Contrastive Learning and Multi-choice Negative Sampling Recommendation. Firstly, an improved topology-aware pruning strategy is used to process the user-item bipartite graph, which uses the topology information of the ...
we develop a new family ofunsupervised sampling methods用于 hard negative samples,有多hard可由使用者自己决定。representation 会将相同的类聚在一起,不同的类尽可能被推得很远。所提出的方法只需要很少的额外代码行即可实现,并且不引入计算开销。 引言 contrastive learning methods已经成为学习表征的最流行的自我监...
本模型的对比正则化由无监督对比和有监督对比组成。从而引出我们接下来的步骤: 对于无监督对比,我们需要Augmentation。 对于有监督对比,我们需要Positive Sampling与Negative Sampling。 无监督增强 由于数据增强操作(item cropping, masking and reordering)不能提供高语义相似度的保证,增强序列并不一定与原序列很相似。所以...
Contrastive learning is a discriminative representation learning framework in computer science that aims to train a feature extractor without the need for labels. It involves minimizing the distance between positive examples and anchor examples, while maximizing the distance between negative examples and anc...
Through minimization of an appropriate loss function such as the InfoNCE loss, contrastive learning (CL) learns a useful representation function by pulling positive samples close to each other while pushing negative samples far apart in the embedding space. The positive samples are typically created us...
The present work solves domain generalization by a joint contrastive learning and adversarial learning.The model adopts contrastive learning by the negative instances sampled from different classes instead of the random negative sampling strategy.The theoretical analysis is provided based on the class-aware...
[Word2vec Parameter Learning Explained] [Efficient Estimation of Word Representation in Vector Space] [Distributed Representations of Words and Phrases and their Compositionality] [Notes on Noise Contrastive Estimation and Negative Sampling]
Multi-Scale Subgraph Contrastive Learning 多尺度子图对比学习 简述：作者发现在图结构中进行数据增强后，...
reveals the inherent tolerance of contrastive learning (CL) towards sampling bias, wherein negative ...