Deep Active Learning with Contrastive Learning Under Realistic Data Pool
Assumptions
- URL: http://arxiv.org/abs/2303.14433v1
- Date: Sat, 25 Mar 2023 10:46:10 GMT
- Title: Deep Active Learning with Contrastive Learning Under Realistic Data Pool
Assumptions
- Authors: Jihyo Kim, Jeonghyeon Kim, Sangheum Hwang
- Abstract summary: Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly.
Most existing active learning methods have been evaluated in an ideal setting where only samples relevant to the target task exist in an unlabeled data pool.
We introduce new active learning benchmarks that include ambiguous, task-irrelevant out-of-distribution as well as in-distribution samples.
- Score: 2.578242050187029
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Active learning aims to identify the most informative data from an unlabeled
data pool that enables a model to reach the desired accuracy rapidly. This
benefits especially deep neural networks which generally require a huge number
of labeled samples to achieve high performance. Most existing active learning
methods have been evaluated in an ideal setting where only samples relevant to
the target task, i.e., in-distribution samples, exist in an unlabeled data
pool. A data pool gathered from the wild, however, is likely to include samples
that are irrelevant to the target task at all and/or too ambiguous to assign a
single class label even for the oracle. We argue that assuming an unlabeled
data pool consisting of samples from various distributions is more realistic.
In this work, we introduce new active learning benchmarks that include
ambiguous, task-irrelevant out-of-distribution as well as in-distribution
samples. We also propose an active learning method designed to acquire
informative in-distribution samples in priority. The proposed method leverages
both labeled and unlabeled data pools and selects samples from clusters on the
feature space constructed via contrastive learning. Experimental results
demonstrate that the proposed method requires a lower annotation budget than
existing active learning methods to reach the same level of accuracy.
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