Supervised Stochastic Neighbor Embedding Using Contrastive Learning
Stochastic neighbor embedding (SNE) methods $t$-SNE, UMAP are two most popular dimensionality reduction methods for data visualization. Contrastive learning, especially self-supervised contrastive learning (SSCL), has showed great success in embedding features from unlabeled data. The conceptual connection between SNE and SSCL has been exploited. In this work, within the scope of preserving neighboring information of a dataset, we extend the self-supervised contrastive approach to the fully-supervised setting, allowing us to effectively leverage label information. Clusters of samples belonging to the same class are pulled together in low-dimensional embedding space, while simultaneously pushing apart clusters of samples from different classes.
Code (1)
Tasks
Contrastive LearningData VisualizationDimensionality ReductionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding o…
Contrastive LearningData AugmentationData VisualizationOut-of-Distribution GeneralizationUnsupervised visualization of image datasets using contrastive learning
Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningful results if the nearest neighbors them…
Contrastive LearningData AugmentationUnsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing
Compared to supervised cross-modal hashing (CMH), unsupervised CMH reduces the reliance on manual labeling by learning binary codes from unlabeled image-text pairs. However, existing unsupervised CMH methods often rely o…
Cross-Modal RetrievalContrastive LearningRevisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
As a seminal tool in self-supervised representation learning, contrastive learning has gained unprecedented attention in recent years. In essence, contrastive learning aims to leverage pairs of positive and negative samp…
Contrastive LearningRepresentation LearningpNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems
Nearest neighbor (NN) sampling provides more semantic variations than pre-defined transformations for self-supervised learning (SSL) based image recognition problems. However, its performance is restricted by the quality…
Contrastive LearningRepresentation LearningSelf-Supervised Learning