# Active and Incremental Learning with Weak Supervision

arXiv: https://arxiv.org/abs/2001.07100 · HTML: https://mlsanity.com/paper/2001.07100/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2020.

## Built on by (most cited first, 8 of 8)

- [Language Models in the Loop: Incorporating Prompting into Weak Supervision](https://mlsanity.com/paper/2205.02318/) (2022)
- [Active WeaSuL: Improving Weak Supervision with Active Learning](https://mlsanity.com/paper/2104.14847/) (2021)
- [Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification](https://mlsanity.com/paper/2307.15427/) (2023)
- [Incremental Learning for Animal Pose Estimation using RBF k-DPP](https://mlsanity.com/paper/2110.13598/) (2021)
- [ACIL: Active Class Incremental Learning for Image Classification](https://mlsanity.com/paper/2602.04252/) (2026)
- [OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis](https://mlsanity.com/paper/2110.02069/) (2021)
- [Active Transfer Bagging: A New Approach for Accelerated Active Learning Acquisition of Data by Combined Transfer Learning and Bagging Based Models](https://mlsanity.com/paper/2602.02415/) (2026)
- [Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference](https://mlsanity.com/paper/2609.27409/) (2026)

## Abstract (arXiv)

Large amounts of labeled training data are one of the main contributors to the great success that deep models have achieved in the past. Label acquisition for tasks other than benchmarks can pose a challenge due to requirements of both funding and expertise. By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost. In this work, we describe combinations of an incremental learning scheme and methods of active learning. These allow for continuous exploration of newly observed unlabeled data. We describe selection criteria based on model uncertainty as well as expected model output change (EMOC). An object detection task is evaluated in a continuous exploration context on the PASCAL VOC dataset. We also validate a weakly supervised system based on active and incremental learning in a real-world biodiversity application where images from camera traps are analyzed. Labeling only 32 images by accepting or rejecting proposals generated by our method yields an increase in accuracy from 25.4% to 42.6%.