6 papers
High Rank Matrix Completion via Grassmannian Proxy Fusion
Huanran Li, Jeremy Johnson, Daniel Pimentel-Alarcón
This paper approaches high-rank matrix completion (HRMC) by filling missing entries in a data matrix where columns lie near a union of subspaces, clustering these columns, and iden…
Subspace Clustering on Incomplete Data with Self-Supervised Contrastive Learning
Huanran Li, Daniel Pimentel-Alarcón
Subspace clustering aims to group data points that lie in a union of low-dimensional subspaces and finds wide application in computer vision, hyperspectral imaging, and recommendat…
Semi-Supervised Contrastive Learning with Orthonormal Prototypes
Huanran Li, Manh Nguyen, Daniel Pimentel-Alarcón
Contrastive learning has emerged as a powerful method in deep learning, excelling at learning effective representations through contrasting samples from different distributions. Ho…
From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling
Jinhong Lin, Cheng-En Wu, Huanran Li +3
Masked Image Modeling (MIM) has emerged as a powerful self-supervised learning paradigm for visual representation learning, enabling models to acquire rich visual representations b…
Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)
Huanran Li, Manh Nguyen, Daniel Pimentel-Alarcón
Contrastive learning has emerged as a powerful method in deep learning, excelling at learning effective representations through contrasting samples from different distributions. Ho…
Deep Fusion: Capturing Dependencies in Contrastive Learning via Transformer Projection Heads
Huanran Li, Daniel Pimentel-Alarcón
Contrastive Learning (CL) has emerged as a powerful method for training feature extraction models using unlabeled data. Recent studies suggest that incorporating a linear projectio…