Why autoencoders can be useful in creative practice?
Autoencoders are a creative application of deep learning to unsupervised problems; an important answer to the quickly growing amount of unlabeled data. Standard autoencoders can be used for anomaly detection or image denoising (when substituting with convolutional layers).
How do I reduce AutoEncoder loss?
1 Answer
- Reduce mini-batch size.
- Try to make the layers have units with expanding/shrinking order.
- The absolute value of the error function.
- This is a bit more tinfoil advice of mine but you also try to shift your numbers down so that the range is -128 to 128.
Is the PCA algorithm the same as the autoencoder?
Like the Autoencoder model, Principal Components Analysis (PCA) is also widely used as a dimensionality reduction technique. However, the PCA algorithm maps the input data differently than the Autoencoder does.
When to use autoencoders in machine learning projects?
In machine learning projects we often run into curse of dimensionality problem where the number of records of data are not a substantial factor of the number of features. This often leads to a problems since it means training a lot of parameters using a scarce data set, which can easily lead to overfitting and poor generalization.
How does PCA work and how does it work?
PCA works by projecting input data onto the eigenvectors of the data’s covariance matrix. The covariance matrix quantifies the variance of the data and how much each variable varies with respect to one another.
What are the principal components of PCA algorithms?
The principal components resulting from PCA are linear combinations of the input variables — just like the glued Lego pieces are linear combinations of the originals. The linear nature of these principal components also allow us to interpret the transformed data. Incapable of learning non-linear feature representations