提出无监督动态头方法，提升少样本分类对噪声和任务差异的鲁棒性。
Unsupervised Meta-Learning via Dynamic Head and Heterogeneous Task Construction for Few-Shot Classification
- 用DBSCAN与动态头部构建异构任务，实现无监督元学习
- 在多个零样本/少样本数据集上达当前最优性能
- 适合研究元学习鲁棒性或无监督少样本分类的学者
近年来元学习广泛应用于少样本学习与强化学习等领域。然而，其在少样本分类中为何且何时优于其他算法仍待探索。本文通过调节数据集中的标签噪声比例与任务异质性进行预实验，利用奇异向量典型相关分析量化神经网络表示稳定性，比较元学习与经典学习算法的行为。结果表明，得益于双层优化策略，元学习算法对标签噪声和异质任务具有更强鲁棒性。基于此结论，我们主张元学习在无监督领域有广阔前景，提出DHM-UHT：一种基于无监督异构任务构造的动态头元学习算法。核心思想是利用DBSCAN与动态头部实现异构任务构建，并元学习整个无监督异构任务构造过程。在多个无监督零样本与少样本数据集上，该方法取得当前最优性能。代码已开源：https://github.com/tuantuange/DHM-UHT。
原文摘要 · Abstract (English)
Meta-learning has been widely used in recent years in areas such as few-shot learning and reinforcement learning. However, the questions of why and when it is better than other algorithms in few-shot classification remain to be explored. In this paper, we perform pre-experiments by adjusting the proportion of label noise and the degree of task heterogeneity in the dataset. We use the metric of Singular Vector Canonical Correlation Analysis to quantify the representation stability of the neural network and thus to compare the behavior of meta-learning and classical learning algorithms. We find that benefiting from the bi-level optimization strategy, the meta-learning algorithm has better robustness to label noise and heterogeneous tasks. Based on the above conclusion, we argue a promising future for meta-learning in the unsupervised area, and thus propose DHM-UHT, a dynamic head meta-learning algorithm with unsupervised heterogeneous task construction. The core idea of DHM-UHT is to use DBSCAN and dynamic head to achieve heterogeneous task construction and meta-learn the whole process of unsupervised heterogeneous task construction. On several unsupervised zero-shot and few-shot datasets, DHM-UHT obtains state-of-the-art performance. The code is released at https://github.com/tuantuange/DHM-UHT.
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