# Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders

arXiv: https://arxiv.org/abs/2008.01214 · HTML: https://mlsanity.com/paper/2008.01214/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Long-term Causal Effects Estimation via Latent Surrogates Representation Learning](https://mlsanity.com/paper/2208.04589/) (2022)
- [Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation](https://mlsanity.com/paper/2110.12635/) (2021)
- [RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2409.00511/) (2024)
- [Meta-autoencoders: An approach to discovery and representation of relationships between dynamically evolving classes](https://mlsanity.com/paper/2507.09362/) (2025)
- [Coupled Transformer Autoencoder for Disentangling Multi-Region Neural Latent Dynamics](https://mlsanity.com/paper/2510.20068/) (2025)
- [End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting](https://mlsanity.com/paper/2601.20846/) (2026)

## Abstract (arXiv)

Domain adaptation approaches aim to exploit useful information from the source domain where supervised learning examples are easier to obtain to address a learning problem in the target domain where there is no or limited availability of such examples. In classification problems, domain adaptation has been studied under varying supervised, unsupervised and semi-supervised conditions. However, a common situation when the labelled samples are available for a subset of target domain classes has been overlooked. In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source domain samples as semantic representations for zero-shot learning. For this particular problem, neither conventional domain adaptation approaches nor zero-shot learning algorithms directly apply. To address this generalized zero-shot domain adaptation problem, we present a novel Coupled Conditional Variational Autoencoder (CCVAE) which can generate synthetic target domain features for unseen classes from their source domain counterparts. Extensive experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security. The results demonstrate the effectiveness of our proposed approach both against established benchmarks and in terms of real-world applicability.