Generative Models
Denoising Autoencoder
A Denoising Autoencoder is a modification on the autoencoder to prevent the network learning the identity function. Specifically, if the autoencoder is too big, then it can just learn the data, so the output equals the input, and does not perform any useful representation learning or dimensionality reduction. Denoising autoencoders solve this problem by corrupting the input data on purpose, adding noise or masking some of the input values.
Image Credit: Kumar et al
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Denoising | 155 | 36.90% |
| General Classification | 10 | 2.38% |
| Translation | 9 | 2.14% |
| Anomaly Detection | 7 | 1.67% |
| Imputation | 6 | 1.43% |
| Dimensionality Reduction | 6 | 1.43% |
| Time Series Analysis | 6 | 1.43% |
| Language Modelling | 6 | 1.43% |
| Domain Adaptation | 6 | 1.43% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
