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Related Experiment Video

Updated: Oct 1, 2025

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Deep adversarial transition learning using cross-grafted generative stacks.

Jinyong Hou¹, Xuejie Ding¹, Jeremiah D Deng¹

¹University of Otago, 60 Clyde Street, Dunedin, New Zealand.

Neural Networks : the Official Journal of the International Neural Network Society

|March 5, 2022

Summary

This summary is machine-generated.

Deep adversarial transition learning (DATL) bridges domain gaps in computer vision by creating intermediate spaces. This novel framework enhances transfer learning by training and testing in these transitional spaces, outperforming existing methods.

Keywords:

Domain adaptation Generative adversarial networks Transfer learning Variational auto-encoders

Related Experiment Videos

Last Updated: Oct 1, 2025

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Traditional deep domain adaptation focuses on domain-invariant features, with limited success in transfer learning.
Bridging the domain gap remains a challenge in unsupervised domain adaptation.

Purpose of the Study:

To introduce a novel deep adversarial transition learning (DATL) framework.
To address the limitations of existing domain adaptation methods by generating intermediate spaces.

Main Methods:

Utilizes variational auto-encoders (VAEs) for source and target domains.
Employs cross-grafted decoder stacks for bidirectional transitions.
Applies generative adversarial networks (GANs) for mapping target data to the source label space via transition alignment.

Main Results:

DATL framework successfully bridges the domain gap.
Training and testing are performed in generated transitional spaces.
Outperforms state-of-the-art methods on unsupervised domain adaptation benchmarks.

Conclusions:

DATL offers a new, effective paradigm for deep domain adaptation.
The method enhances transfer learning by leveraging transitional spaces.
Demonstrates superior performance in unsupervised domain adaptation tasks.