An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name.
Extracted from: Wikipedia
Image source: Wikipedia
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Anomaly Detection | 32 | 11.27% |
Dimensionality Reduction | 11 | 3.87% |
Denoising | 10 | 3.52% |
Unsupervised Anomaly Detection | 9 | 3.17% |
Clustering | 9 | 3.17% |
Video Anomaly Detection | 7 | 2.46% |
Decision Making | 6 | 2.11% |
Image Generation | 6 | 2.11% |
Self-Supervised Learning | 6 | 2.11% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |