In this paper, we propose to integrate instance clustering with distant training, and develop a novel clustering-augmented multi-instance training framework. Specifically, for sentences labeled with the same relation type, we jointly perform clustering based on their semantic representations, and treat ...
最近self-supervised learning很火,方法上简单明了,但确实work的很好,尤其是用在clustering或是所谓self-labelling(不需要人工标注,而可以获得label assignment) 任务。本文中,我尝试根据自己的理解,对3篇近期self-supervised representation learning和clustering结合的方法进行如下总结,具体实验部分请参考原文,其中2篇是来自...
后者是offline的,需要两步,第一步给予encoder的输出做个clustering,然后用clustering的结果训练encoder。这需要多次pass over数据集,因此不适合online learning。本文是如何做的呢? 同样是做image transformation。 不同的augmented view 通过encoder学到feature vector 。 将feature vector 映射到一个单位球体,概括如下: 4...
To train the ML model with the augmented dataset, the original dataset was divided into training and testing sets, and the generated data were then added to the training set to train the model. To achieve statistical significance, this process was repeated five times to mimic fivefold cross-va...
15 to learn the feature representations of skin diseases in an unsupervised manner using the online clustering method. SwAV encodes two different augmented views of the same image into features zt and zs respectively. Then a set of trainable code vectors qt and qs are computed by matching these...
distribution can be used for various feature extraction techniques, which is very useful forclustering algorithms. Other applications of unsupervised learning can be the use ofgenerative modelsto synthesize realistic data samples, which have various application in fields such asaugmented realityand so on...
Meta-learning with memory-augmented neural networks. ICML 2016 最早用 external memory 解 FSL classification 的 別名:One-shot Learning with Memory-Augmented Neural Networks. arXiv'16 这篇论文解释了单样本学习与元学习的关系 my paper note Architectures with augmented memory capacities, such as Neural Tu...
The correspondence between augmented pairs is acquired, and the invariant semantic is maintained under perturbations during augmentation. Zhang and Zhu (2019) introduced a simple two-phase unsupervised GCN framework (contrasting and clustering), to capture superior point embedding by solving part contrast...
CL属于表征学习,其本质是学习一种样本的表征方法,学习的目标是让样本与其对应的增强版本的表征或者Embedding相距较近,不同样本的表征或者Embedding之间相距较远。如下图所示,猴子图片与其对应的Augmented版本的表征距离较近,猴子图片与老虎图片的表征距离较远,这种学习任务被称为Pretext Task,具体详见下一节。
Graph Debiased Contrastive Learning with Joint Representation Clustering. In Proceedings of the IJCAI 2021, Virtual, 19–26 August 2021; pp. 3434–3440. [Google Scholar] Yu, L.; Pei, S.; Ding, L.; Zhou, J.; Li, L.; Zhang, C.; Zhang, X. SAIL: Self-Augmented Graph Contrastive ...