Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies

TL;DR

This paper introduces an active learning framework that enhances multi-domain model performance by efficiently selecting informative samples, reducing labeling effort, and outperforming existing methods across various datasets.

Contribution

The paper proposes a novel two-stage active learning approach tailored for multi-domain tasks, demonstrating superior performance and efficiency over baseline methods.

Findings

Outperforms baseline active learning methods on multiple datasets

Requires fewer labeled samples to achieve high performance

Achieves state-of-the-art results in multi-domain classification tasks

Abstract

Improving performance in multiple domains is a challenging task, and often requires significant amounts of data to train and test models. Active learning techniques provide a promising solution by enabling models to select the most informative samples for labeling, thus reducing the amount of labeled data required to achieve high performance. In this paper, we present an active learning-based framework for improving performance across multiple domains. Our approach consists of two stages: first, we use an initial set of labeled data to train a base model, and then we iteratively select the most informative samples for labeling to refine the model. We evaluate our approach on several multi-domain datasets, including image classification, sentiment analysis, and object recognition. Our experiments demonstrate that our approach consistently outperforms baseline methods and achieves state-of-the-art performance on several datasets. We also show that our method is highly efficient, requiring significantly fewer labeled samples than other active learning-based methods. Overall, our approach provides a practical and effective solution for improving performance across multiple domains using active learning techniques.