The document provides a review of transfer learning, a machine learning method that leverages knowledge from solving one problem to address related problems. It discusses various categories of transfer learning, including homogeneous and heterogeneous transfer, and explores its connections with semi-supervised, multiview, and multitask learning. Additionally, it highlights applications of transfer learning in fields such as medicine, bioinformatics, transportation, and recommender systems.

1. Paper Review on Transfer Learning
 -BY: SWATHI ANANDRAM
 PAPER LINK: HTTPS://IEEEXPLORE-IEEE-
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 MEDIUM ARTICLE LINK: HTTPS://MEDIUM.COM/P/89115607E91#EF9B-B9B934015CFC

2. What is Transfer Learning?
 It is a method under research in
Machine Learning that stores the
knowledge obtained from solving
one problem and uses it to solve
problems that are different but
related to the solved one.

3. Real life example

4. Categories based on differences in domains
Homogeneous transfer
It handles situations where the domains are
of similar feature space.
Heterogeneous transfer
It handles situations with domains of
different feature spaces.

5. Connection between Transfer Learning and Machine
Learning tasks
Semi-supervised
learning
Multiview
Learning
Multitask
Learning

6. Categories of Transfer Learning

7. Strategies and objectives of transfer learning
from a data perspective

8. Strategies and objectives of transfer learning from a
model perspective

9. Application of Transfer Learning
Medical
Application
Bioinformatics
Application
Transportation
Application
Recommender
System
Application