k-Means Clustering is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$ different centroids {$\mu\left(1\right),\ldots,\mu\left(k\right)$} to different values, then alternating between two steps until convergence:
(i) each training example is assigned to cluster $i$ where $i$ is the index of the nearest centroid $\mu^{(i)}$
(ii) each centroid $\mu^{(i)}$ is updated to the mean of all training examples $x^{(j)}$ assigned to cluster $i$.
Text Source: Deep Learning, Goodfellow et al
Image Source: scikit-learn
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Object Detection | 101 | 16.48% |
General Classification | 36 | 5.87% |
Semantic Segmentation | 32 | 5.22% |
BIG-bench Machine Learning | 21 | 3.43% |
Image Classification | 19 | 3.10% |
Real-Time Object Detection | 14 | 2.28% |
Autonomous Driving | 13 | 2.12% |
Management | 10 | 1.63% |
Instance Segmentation | 8 | 1.31% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |