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
Most implemented papers
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.
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.
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.
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.
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.
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.
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.
Federated Online Clustering of Bandits
Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems.
Online Arbitrary Shaped Clustering through Correlated Gaussian Functions
There is no convincing evidence that backpropagation is a biologically plausible mechanism, and further studies of alternative learning methods are needed.
Probabilistic Back-ends for Online Speaker Recognition and Clustering
sholokhovalexey/online-speaker-clustering
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This paper focuses on multi-enrollment speaker recognition which naturally occurs in the task of online speaker clustering, and studies the properties of different scoring back-ends in this scenario.
