Towards General and Efficient Active Learning
- URL: http://arxiv.org/abs/2112.07963v1
- Date: Wed, 15 Dec 2021 08:35:28 GMT
- Title: Towards General and Efficient Active Learning
- Authors: Yichen Xie, Masayoshi Tomizuka, Wei Zhan
- Abstract summary: Active learning aims to select the most informative samples to exploit limited annotation budgets.
We propose a novel general and efficient active learning (GEAL) method in this paper.
Our method can conduct data selection processes on different datasets with a single-pass inference of the same model.
- Score: 20.888364610175987
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Active learning aims to select the most informative samples to exploit
limited annotation budgets. Most existing work follows a cumbersome pipeline by
repeating the time-consuming model training and batch data selection multiple
times on each dataset separately. We challenge this status quo by proposing a
novel general and efficient active learning (GEAL) method in this paper.
Utilizing a publicly available model pre-trained on a large dataset, our method
can conduct data selection processes on different datasets with a single-pass
inference of the same model. To capture the subtle local information inside
images, we propose knowledge clusters that are easily extracted from the
intermediate features of the pre-trained network. Instead of the troublesome
batch selection strategy, all data samples are selected in one go by performing
K-Center-Greedy in the fine-grained knowledge cluster level. The entire
procedure only requires single-pass model inference without training or
supervision, making our method notably superior to prior arts in terms of time
complexity by up to hundreds of times. Extensive experiments widely demonstrate
the promising performance of our method on object detection, semantic
segmentation, depth estimation, and image classification.
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