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
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition
canbaoburen/CoDT
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Furthermore, to leverage the complementarity of domain-shared features and target-specific features, we propose a novel collaborative clustering strategy to enforce pair-wise relationship consistency between the two branches.
Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters
eidheim/gaussian-neurons-for-online-clustering
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Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversarial attacks.
Towards Self-Supervised Gaze Estimation
Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification.
Efficient Deep Embedded Subspace Clustering
jinyucai95/edesc-pytorch
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CVPR 2022
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.
Large-Scale Hyperspectral Image Clustering Using Contrastive Learning
angrycai/sscc
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Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool.
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation
sweety-dm/interference-aware-deep-q-learning
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In this paper, we present IQ, i. e., interference-aware deep Q-learning, to mitigate catastrophic interference in single-task deep reinforcement learning.
Unsupervised Visual Representation Learning by Online Constrained K-Means
idstcv/coke
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CVPR 2022
Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination.
Group-aware Label Transfer for Domain Adaptive Person Re-identification
zkcys001/UDAStrongBaseline
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CVPR 2021
In this paper, we propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning.
Contrastive Clustering
Yunfan-Li/Contrastive-Clustering
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In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Kaixhin/EC
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Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches.
