# DEVDAN: Deep Evolving Denoising Autoencoder

arXiv: https://arxiv.org/abs/1910.04062 · HTML: https://mlsanity.com/paper/1910.04062/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2019.

## Built on by (most cited first, 16 of 16)

- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [Unsupervised Continual Learning in Streaming Environments](https://mlsanity.com/paper/2109.09282/) (2021)
- [Learning Interpretation with Explainable Knowledge Distillation](https://mlsanity.com/paper/2111.06945/) (2021)
- [Adaptive Online Incremental Learning for Evolving Data Streams](https://mlsanity.com/paper/2201.01633/) (2022)
- [Automatic Online Multi-Source Domain Adaptation](https://mlsanity.com/paper/2109.01996/) (2021)
- [Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping](https://mlsanity.com/paper/2204.11291/) (2022)
- [Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams](https://mlsanity.com/paper/2107.02943/) (2021)
- [Feature-aware conditional GAN for category text generation](https://mlsanity.com/paper/2308.00939/) (2023)
- [Self-evolving Autoencoder Embedded Q-Network](https://mlsanity.com/paper/2402.11604/) (2024)
- [Quantile Online Learning for Semiconductor Failure Analysis](https://mlsanity.com/paper/2303.07062/) (2023)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [Xp-GAN: Unsupervised Multi-object Controllable Video Generation](https://mlsanity.com/paper/2111.10233/) (2021)
- [Online Deep Learning based on Auto-Encoder](https://mlsanity.com/paper/2201.07383/) (2022)
- [DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data](https://mlsanity.com/paper/2307.04075/) (2023)
- [Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler](https://mlsanity.com/paper/2310.17817/) (2023)
- [Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering](https://mlsanity.com/paper/2402.09167/) (2024)

## Abstract (arXiv)

The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves an in-depth study because it characterizes a fixed network capacity that cannot adapt to rapidly changing environments. Deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features an open structure in the generative phase and the discriminative phase where the hidden units can be automatically added and discarded on the fly. The generative phase refines the predictive performance of the discriminative model exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific threshold and works fully in the single-pass learning fashion. We show that DEVDAN can find competitive network architecture compared with state-of-the-art methods on the classification task using ten prominent datasets simulated under the prequential test-then-train protocol.