Prototype-Anchored Learning for Learning with Imperfect Annotations
- URL: http://arxiv.org/abs/2206.11602v1
- Date: Thu, 23 Jun 2022 10:25:37 GMT
- Title: Prototype-Anchored Learning for Learning with Imperfect Annotations
- Authors: Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao,
Xiangyang Ji
- Abstract summary: It is challenging to learn unbiased classification models from imperfectly annotated datasets.
We propose a prototype-anchored learning (PAL) method, which can be easily incorporated into various learning-based classification schemes.
We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
- Score: 83.7763875464011
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The success of deep neural networks greatly relies on the availability of
large amounts of high-quality annotated data, which however are difficult or
expensive to obtain. The resulting labels may be class imbalanced, noisy or
human biased. It is challenging to learn unbiased classification models from
imperfectly annotated datasets, on which we usually suffer from overfitting or
underfitting. In this work, we thoroughly investigate the popular softmax loss
and margin-based loss, and offer a feasible approach to tighten the
generalization error bound by maximizing the minimal sample margin. We further
derive the optimality condition for this purpose, which indicates how the class
prototypes should be anchored. Motivated by theoretical analysis, we propose a
simple yet effective method, namely prototype-anchored learning (PAL), which
can be easily incorporated into various learning-based classification schemes
to handle imperfect annotation. We verify the effectiveness of PAL on
class-imbalanced learning and noise-tolerant learning by extensive experiments
on synthetic and real-world datasets.
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