Deep learning and transfer learning

We work across a number of deep learning paradigms. We're leaders in the automated design of deep learning models, including deep neural networks.

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Deep learning paradigms

We work on a number of different deep learning paradigms, including:

neural network based deep learning such as deep convolutional neural networks
deep auto-encoders
deep generative adversarial networks, deep belief networks, and their variants
non-neural network-based deep learning such as:
- GP-based deep learning
- deep PCAs
- deep forest learning.

Automated design

Te Herenga Waka—Victoria University of Wellington is particularly good in automated design of deep learning models.

This includes deep neural networks and other deep models, where the structure or architecture and parameters are learned and optimised automatically and simultaneously.

Transfer learning

Transfer learning is also a big area of the Centre, including:

domain adaptation
domain generalisation
multi-task learning and optimisation.

Learning paradigms

The work varies with different learning paradigms such as:

evolutionary computation techniques
neural networks
kerne-based learning or symbolic learning algorithms.

Related research groups

Evolutionary computation and machine learning

We focus on topics such as genetic programming, learning classifier systems, particle swarm optimisation, evolutionary computer vision, and image analysis.

Explore research groups