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Active Learning With Long-Range Observation
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AI
DOI：10.1109/LSP.2024.3435417.png)
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Abstract
En 中文
In the era of data-driven technological advancements, deep learning still craves more training data. However, the high cost of data annotation and limited budgets pose significant challenges. To address this issue, active learning (AL) has emerged, and it gradually adds some informative samples to the training data by querying humans for annotation. Existing works have mainly focused on how to sample useful data based on the estimations of the model in the current cycle (time). However, models in different cycles have distinct knowledge and biases by training with the expanding dataset. Also, relying solely on a present bias is not necessarily the best choice in the real world, where the knowledge of the present model is distorted by labeling attacks or mislabeling situations. Here, we propose a novel AL approach that expands viewpoint and knowledge with the strong committee having long-range observation for seeking informative data. The committee is designed to reflect estimations of all previous and current models and sample data points by selectively aggregating model estimations. By exploiting various trajectories of multiple models and broadening knowledge, it can overcome limited perspectives and potential shortcomings of the current estimation. We validate the proposed approach under ideal and realistic scenarios with coarse-grained and fined-grained image classification tasks. In experimental results, the proposed method outperforms recent competitive methods for six settings, including realistic and ideal scenarios.
Keywords:
Estimation
Training
Training data
Data models
Annotations
Uncertainty
Loss measurement
Active learning
long-range observation
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
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