# Learning latent representations across multiple data domains using Lifelong VAEGAN

arXiv: https://arxiv.org/abs/2007.10221 · HTML: https://mlsanity.com/paper/2007.10221/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 30 of 35)

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- [Continual Learning of Natural Language Processing Tasks: A Survey](https://mlsanity.com/paper/2211.12701/) (2022)
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- [Recent Advances of Multimodal Continual Learning: A Comprehensive Survey](https://mlsanity.com/paper/2410.05352/) (2024)
- [The Challenges of Continuous Self-Supervised Learning](https://mlsanity.com/paper/2203.12710/) (2022)
- [Continual Learning with Low Rank Adaptation](https://mlsanity.com/paper/2311.17601/) (2023)
- [DiffClass: Diffusion-Based Class Incremental Learning](https://mlsanity.com/paper/2403.05016/) (2024)
- [Task-Free Continual Learning via Online Discrepancy Distance Learning](https://mlsanity.com/paper/2210.06579/) (2022)
- [Task-agnostic Continual Learning with Hybrid Probabilistic Models](https://mlsanity.com/paper/2106.12772/) (2021)
- [SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge](https://mlsanity.com/paper/2208.11266/) (2022)
- [Exploring Continual Learning of Diffusion Models](https://mlsanity.com/paper/2303.15342/) (2023)
- [Continual Variational Autoencoder Learning via Online Cooperative Memorization](https://mlsanity.com/paper/2207.10131/) (2022)
- [Lifelong Mixture of Variational Autoencoders](https://mlsanity.com/paper/2107.04694/) (2021)
- [Adaptive Visual Scene Understanding: Incremental Scene Graph Generation](https://mlsanity.com/paper/2310.01636/) (2023)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [InfoVAEGAN : learning joint interpretable representations by information maximization and maximum likelihood](https://mlsanity.com/paper/2107.04705/) (2021)
- [Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process](https://mlsanity.com/paper/2108.12278/) (2021)
- [Lifelong Twin Generative Adversarial Networks](https://mlsanity.com/paper/2107.04708/) (2021)
- [Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning](https://mlsanity.com/paper/2106.12196/) (2021)
- [Lifelong Generative Modelling Using Dynamic Expansion Graph Model](https://mlsanity.com/paper/2112.08370/) (2021)
- [Learning an evolved mixture model for task-free continual learning](https://mlsanity.com/paper/2207.05080/) (2022)
- [Adiabatic replay for continual learning](https://mlsanity.com/paper/2303.13157/) (2023)
- [Transfer Learning via Test-Time Neural Networks Aggregation](https://mlsanity.com/paper/2206.13399/) (2022)
- [Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning](https://mlsanity.com/paper/2207.12831/) (2022)
- [An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning](https://mlsanity.com/paper/2211.16780/) (2022)
- [A Benchmark Grocery Dataset of Realworld Point Clouds From Single View](https://mlsanity.com/paper/2402.07819/) (2024)
- [Flashbacks to Harmonize Stability and Plasticity in Continual Learning](https://mlsanity.com/paper/2506.00477/) (2025)
- [Continual Learning for Multimodal Data Fusion of a Soft Gripper](https://mlsanity.com/paper/2409.13792/) (2024)

## Abstract (arXiv)

The problem of catastrophic forgetting occurs in deep learning models trained on multiple databases in a sequential manner. Recently, generative replay mechanisms (GRM), have been proposed to reproduce previously learned knowledge aiming to reduce the forgetting. However, such approaches lack an appropriate inference model and therefore can not provide latent representations of data. In this paper, we propose a novel lifelong learning approach, namely the Lifelong VAEGAN (L-VAEGAN), which not only induces a powerful generative replay network but also learns meaningful latent representations, benefiting representation learning. L-VAEGAN can allow to automatically embed the information associated with different domains into several clusters in the latent space, while also capturing semantically meaningful shared latent variables, across different data domains. The proposed model supports many downstream tasks that traditional generative replay methods can not, including interpolation and inference across different data domains.