# A Survey on Computational Intelligence-based Transfer Learning

arXiv: https://arxiv.org/abs/2206.10593 · HTML: https://mlsanity.com/paper/2206.10593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Label Efficient Learning of Transferable Representations across Domains and Tasks](https://mlsanity.com/paper/1712.00123/) (2017)
- [Transfer Learning based Dynamic Multiobjective Optimization Algorithms](https://mlsanity.com/paper/1612.06093/) (2016)
- [RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks](https://mlsanity.com/paper/1802.06403/) (2018)
- [Causal Generative Domain Adaptation Networks](https://mlsanity.com/paper/1804.04333/) (2018)
- [DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification](https://mlsanity.com/paper/1812.11478/) (2018)
- [Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection](https://mlsanity.com/paper/2109.15136/) (2021)
- [Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization](https://mlsanity.com/paper/1607.05390/) (2016)

## Abstract (arXiv)

The goal of transfer learning (TL) is providing a framework for exploiting acquired knowledge from source to target data. Transfer learning approaches compared to traditional machine learning approaches are capable of modeling better data patterns from the current domain. However, vanilla TL needs performance improvements by using computational intelligence-based TL. This paper studies computational intelligence-based transfer learning techniques and categorizes them into neural network-based, evolutionary algorithm-based, swarm intelligence-based and fuzzy logic-based transfer learning.