Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based
Multi-Label Classification of Remote Sensing Images
- URL: http://arxiv.org/abs/2306.06908v2
- Date: Wed, 21 Jun 2023 07:52:47 GMT
- Title: Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based
Multi-Label Classification of Remote Sensing Images
- Authors: Lars M\"ollenbrok and Beg\"um Demir
- Abstract summary: Self-supervised pre-training combined with fine-tuning on a randomly selected small training set has become a popular approach to minimize annotation efforts.
We investigate the effectiveness of the joint use of self-supervised pre-training with active learning (AL)
Experimental results show the effectiveness of applying AL-guided fine-tuning compared to the application of fine-tuning using a randomly constructed small training set.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, deep neural networks (DNNs) have been found very successful
for multi-label classification (MLC) of remote sensing (RS) images.
Self-supervised pre-training combined with fine-tuning on a randomly selected
small training set has become a popular approach to minimize annotation efforts
of data-demanding DNNs. However, fine-tuning on a small and biased training set
may limit model performance. To address this issue, we investigate the
effectiveness of the joint use of self-supervised pre-training with active
learning (AL). The considered AL strategy aims at guiding the MLC fine-tuning
of a self-supervised model by selecting informative training samples to
annotate in an iterative manner. Experimental results show the effectiveness of
applying AL-guided fine-tuning (particularly for the case where strong
class-imbalance is present in MLC problems) compared to the application of
fine-tuning using a randomly constructed small training set.
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