Autoencoder
Created: 2022-03-24 10:16
#note
An autoencoder is a neural network that reconstructs its input data in the output layer-> the encoder maps the input to an internal representation and the decoder that maps this representation to a reconstruction of the input. This architecture contrasts with Adversarial Networks, which use competing network objectives.
Pros: #dimensionality_reduction, #data_reconstruction, #feature_extraction
Two approaches:
- usning autoencoders to learn lower-dimensional feature representations at the bottleneck layer;
- filling the blanks of the interaction matrix directly in the reconstruction layer
Types:
- Denoising AE;
- Variational AE;
- Contractive AE;
- Marginalized AE
References
- https://medium.com/sciforce/deep-learning-based-recommender-systems-b61a5ddd5456
Tags
#autoencoder