Transfer Learning vs. Domain Adaptation Following quoted from Wikipedia Domain adaptation is the ability to apply an algorithm trained in one or more “source domains” to a different (but related) “target domain”. Domain adaptation is a subcategory of transfer learning. In domain adaptation, the source and target domains all have the same feature space (but …
Manifold Hypothesis[1] Real-world high dimensional data (such as images) lie on low-dimensional manifolds embedded in the high-dimensional space. Chapter 14.6 Deep Learning Book[3] Like many other machine learning algorithms, autoencoders exploit the idea that data concentrates around a low-dimentional manifold or a small set of such manifolds. Some machine learning algorithms exploit this ideal only …
Mitesh M. Khapra Lecture PDF Introduction to Autoencoders (Video) Link between PCA and Autoencoders (Video) Regularization in autoencoders (Motivation) (Video) Denoising Autoencoders (Video) Sparse Autoencoders (Video) Contractive autoencoders (Video) Hugo Larochelle Autoencoder – definition (Video) Autoencoder – loss function (Video) Example (Video) Linear Autoencoder (Video) Autoencoder – undercomplete vs. overcomplete hidden layer (Video) Autoencoder – …
