Perturbation-Based Two-Stage Multi-Domain Active Learning

by   Rui He, et al.

In multi-domain learning (MDL) scenarios, high labeling effort is required due to the complexity of collecting data from various domains. Active Learning (AL) presents an encouraging solution to this issue by annotating a smaller number of highly informative instances, thereby reducing the labeling effort. Previous research has relied on conventional AL strategies for MDL scenarios, which underutilize the domain-shared information of each instance during the selection procedure. To mitigate this issue, we propose a novel perturbation-based two-stage multi-domain active learning (P2S-MDAL) method incorporated into the well-regarded ASP-MTL model. Specifically, P2S-MDAL involves allocating budgets for domains and establishing regions for diversity selection, which are further used to select the most cross-domain influential samples in each region. A perturbation metric has been introduced to evaluate the robustness of the shared feature extractor of the model, facilitating the identification of potentially cross-domain influential samples. Experiments are conducted on three real-world datasets, encompassing both texts and images. The superior performance over conventional AL strategies shows the effectiveness of the proposed strategy. Additionally, an ablation study has been carried out to demonstrate the validity of each component. Finally, we outline several intriguing potential directions for future MDAL research, thus catalyzing the field's advancement.


page 1

page 2

page 3

page 4


Multi-Domain Active Learning: A Comparative Study

Building classifiers on multiple domains is a practical problem in the r...

Rebuilding Trust in Active Learning with Actionable Metrics

Active Learning (AL) is an active domain of research, but is seldom used...

Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection

Unsupervised Domain Adaptation (UDA) technique has been explored in 3D c...

Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data

The paucity of labeled data is a typical challenge in the automotive ind...

ImitAL: Learned Active Learning Strategy on Synthetic Data

Active Learning (AL) is a well-known standard method for efficiently obt...

A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

Pool-based active learning (AL) aims to optimize the annotation process ...

Please sign up or login with your details

Forgot password? Click here to reset