kMeans 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: scikitlearn
Paper  Code  Results  Date  Stars 

Task  Papers  Share 

Clustering  182  18.82% 
Object Detection  125  12.93% 
Semantic Segmentation  25  2.59% 
Autonomous Driving  22  2.28% 
Classification  16  1.65% 
Image Classification  15  1.55% 
SelfSupervised Learning  13  1.34% 
Quantization  12  1.24% 
Decision Making  11  1.14% 
Component  Type 


🤖 No Components Found  You can add them if they exist; e.g. Mask RCNN uses RoIAlign 