Online Clustering
25 papers with code • 0 benchmarks • 0 datasets
Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels. Under the online scenario, data is in the form of streams, i.e., the whole dataset could not be accessed at the same time and the model should be able to make cluster assignments for new data without accessing the former data.
Image Credit: Online Clustering by Penalized Weighted GMM
Benchmarks
These leaderboards are used to track progress in Online Clustering
Latest papers with no code
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students).
Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet.
SubGen: Token Generation in Sublinear Time and Memory
In this work, our focus is on developing an efficient compression technique for the KV cache.
FedGT: Federated Node Classification with Scalable Graph Transformer
However, existing methods have the following limitations: (1) The links between local subgraphs are missing in subgraph federated learning.
Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning
Specifically, a teacher model continually refines pseudo labels through online clustering, providing dynamic supervision signals to train the student model.
Novel class discovery meets foundation models for 3D semantic segmentation
Firstly, it introduces the novel task of NCD for point cloud semantic segmentation.
Neuromorphic Online Clustering and Classification
A single dendrite is then composed of multiple segments and is capable of online clustering.
Online Clustering of Bandits with Misspecified User Models
In this paper, we are the first to present the important problem of clustering of bandits with misspecified user models (CBMUM), where the expected rewards in user models can be perturbed away from perfect linear models.
Clustering-based Domain-Incremental Learning
A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task".
Online Sequence Clustering Algorithm for Video Trajectory Analysis
Target tracking and trajectory modeling have important applications in surveillance video analysis and have received great attention in the fields of road safety and community security.
