Dimensionality Reduction
Autoencoders
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
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Anomaly Detection | 35 | 8.77% |
| Decoder | 27 | 6.77% |
| Denoising | 12 | 3.01% |
| Dimensionality Reduction | 11 | 2.76% |
| Unsupervised Anomaly Detection | 10 | 2.51% |
| Clustering | 10 | 2.51% |
| Deep Learning | 10 | 2.51% |
| Quantization | 7 | 1.75% |
| Image Generation | 7 | 1.75% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
