# SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks

arXiv: https://arxiv.org/abs/2504.04613 · HTML: https://mlsanity.com/paper/2504.04613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 81 references that are in the map.
- First posted on arXiv 6 Apr 2025.

## Builds on (most cited first, 25 of 25)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Dataset Augmentation in Feature Space](https://mlsanity.com/paper/1702.05538/) (2017)
- [Data Augmentation by Pairing Samples for Images Classification](https://mlsanity.com/paper/1801.02929/) (2018)
- [Style Augmentation: Data Augmentation via Style Randomization](https://mlsanity.com/paper/1809.05375/) (2018)
- [Bayesian Generative Active Deep Learning](https://mlsanity.com/paper/1904.11643/) (2019)
- [A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification](https://mlsanity.com/paper/1910.04176/) (2019)
- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [A Systematic Study of Online Class Imbalance Learning with Concept Drift](https://mlsanity.com/paper/1703.06683/) (2017)
- [Online Learning With Adaptive Rebalancing in Nonstationary Environments](https://mlsanity.com/paper/2009.11942/) (2020)
- [Nonstationary data stream classification with online active learning and siamese neural networks](https://mlsanity.com/paper/2210.01090/) (2022)
- [Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation](https://mlsanity.com/paper/2305.08977/) (2023)
- [Deep Active Learning with Augmentation-based Consistency Estimation](https://mlsanity.com/paper/2011.02666/) (2020)
- [Active Weighted Aging Ensemble for Drifted Data Stream Classification](https://mlsanity.com/paper/2112.10150/) (2021)
- [Queue-based Resampling for Online Class Imbalance Learning](https://mlsanity.com/paper/1809.10388/) (2018)
- [Data-efficient Online Classification with Siamese Networks and Active Learning](https://mlsanity.com/paper/2010.01659/) (2020)
- [A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification](https://mlsanity.com/paper/2210.04949/) (2022)
- [Data augmentation on-the-fly and active learning in data stream classification](https://mlsanity.com/paper/2210.06873/) (2022)
- [Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences](https://mlsanity.com/paper/2403.03576/) (2024)
- [Unsupervised Unlearning of Concept Drift with Autoencoders](https://mlsanity.com/paper/2211.12989/) (2022)
- [A Clustering-based Framework for Classifying Data Streams](https://mlsanity.com/paper/2106.11823/) (2021)

## Built on by (most cited first, 1 of 1)

- [Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling](https://mlsanity.com/paper/2602.12976/) (2026)

## Abstract (arXiv)

Data stream mining, also known as stream learning, is a growing area which deals with learning from high-speed arriving data. Its relevance has surged recently due to its wide range of applicability, such as, critical infrastructure monitoring, social media analysis, and recommender systems. The design of stream learning methods faces significant research challenges; from the nonstationary nature of the data (referred to as concept drift) and the fact that data streams are typically not annotated with the ground truth, to the requirement that such methods should process large amounts of data in real-time with limited memory. This work proposes the SiameseDuo++ method, which uses active learning to automatically select instances for a human expert to label according to a budget. Specifically, it incrementally trains two siamese neural networks which operate in synergy, augmented by generated examples. Both the proposed active learning strategy and augmentation operate in the latent space. SiameseDuo++ addresses the aforementioned challenges by operating with limited memory and limited labelling budget. Simulation experiments show that the proposed method outperforms strong baselines and state-of-the-art methods in terms of learning speed and/or performance. To promote open science we publicly release our code and datasets.