# Bayesian Generative Active Deep Learning

arXiv: https://arxiv.org/abs/1904.11643 · HTML: https://mlsanity.com/paper/1904.11643/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

## Built on by (most cited first, 30 of 57)

- [Plex: Towards Reliability using Pretrained Large Model Extensions](https://mlsanity.com/paper/2207.07411/) (2022)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Prediction-Oriented Bayesian Active Learning](https://mlsanity.com/paper/2304.08151/) (2023)
- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Active Learning for Open-set Annotation](https://mlsanity.com/paper/2201.06758/) (2022)
- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection](https://mlsanity.com/paper/2106.11921/) (2021)
- [Exploring Active 3D Object Detection from a Generalization Perspective](https://mlsanity.com/paper/2301.09249/) (2023)
- [Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels](https://mlsanity.com/paper/2206.12569/) (2022)
- [Entropic Open-set Active Learning](https://mlsanity.com/paper/2312.14126/) (2023)
- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [General Intelligence Requires Rethinking Exploration](https://mlsanity.com/paper/2211.07819/) (2022)
- [ScatterShot: Interactive In-context Example Curation for Text Transformation](https://mlsanity.com/paper/2302.07346/) (2023)
- [Which images to label for few-shot medical landmark detection?](https://mlsanity.com/paper/2112.04386/) (2021)
- [TiDAL: Learning Training Dynamics for Active Learning](https://mlsanity.com/paper/2210.06788/) (2022)
- [Making Better Use of Unlabelled Data in Bayesian Active Learning](https://mlsanity.com/paper/2404.17249/) (2024)
- [Model Already Knows the Best Noise: Bayesian Active Noise Selection via Attention in Video Diffusion Model](https://mlsanity.com/paper/2505.17561/) (2025)
- [Bayesian Active Learning by Disagreements: A Geometric Perspective](https://mlsanity.com/paper/2105.02543/) (2021)
- [Improving Adversarial Robustness by Contrastive Guided Diffusion Process](https://mlsanity.com/paper/2210.09643/) (2022)
- [Deep Active Learning for Computer Vision: Past and Future](https://mlsanity.com/paper/2211.14819/) (2022)
- [Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions](https://mlsanity.com/paper/2401.04305/) (2024)
- [Multi-Domain Active Learning: Literature Review and Comparative Study](https://mlsanity.com/paper/2106.13516/) (2021)
- [Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation](https://mlsanity.com/paper/2108.03534/) (2021)
- [OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification](https://mlsanity.com/paper/2307.05254/) (2023)
- [Inconsistency-Based Data-Centric Active Open-Set Annotation](https://mlsanity.com/paper/2401.04923/) (2024)
- [Cohort-Based Active Modality Acquisition](https://mlsanity.com/paper/2505.16791/) (2025)
- [A Survey on Active Deep Learning: From Model-driven to Data-driven](https://mlsanity.com/paper/2101.09933/) (2021)
- [Active Self-Semi-Supervised Learning for Few Labeled Samples](https://mlsanity.com/paper/2203.04560/) (2022)

## Abstract (arXiv)

Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources for training and labeling, constraining the types of problems that can be tackled. Therefore, the design of effective training methods that require small labeled training sets is an important research direction that will allow a more effective use of resources.Among current approaches designed to address this issue, two are particularly interesting: data augmentation and active learning. Data augmentation achieves this goal by artificially generating new training points, while active learning relies on the selection of the "most informative" subset of unlabeled training samples to be labelled by an oracle. Although successful in practice, data augmentation can waste computational resources because it indiscriminately generates samples that are not guaranteed to be informative, and active learning selects a small subset of informative samples (from a large un-annotated set) that may be insufficient for the training process. In this paper, we propose a Bayesian generative active deep learning approach that combines active learning with data augmentation -- we provide theoretical and empirical evidence (MNIST, CIFAR-10,100, and SVHN) that our approach has more efficient training and better classification results than data augmentation and active learning.