Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models

no code implementations • 2 Mar 2024 • Ziting Wen, Oscar Pizarro, Stefan Williams

Fine-tuning the pre-trained model with active learning holds promise for reducing annotation costs.

Active Learning Computational Efficiency

NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage

no code implementations • 7 Jun 2023 • Ziting Wen, Oscar Pizarro, Stefan Williams

Recent research has shown that in the context of supervised learning different active learning strategies need to be applied at various stages of the training process to ensure improved performance over the random baseline.

Active Learning Clustering +2

Training from a Better Start Point: Active Self-Semi-Supervised Learning for Few Labeled Samples

no code implementations • 9 Mar 2022 • Ziting Wen, Oscar Pizarro, Stefan Williams

Consequently, our framework can significantly improve the performance of models in the case of few annotations while reducing the training time.

Active Learning