Few-Shot Partial-Label Learning

• URL: http://arxiv.org/abs/2106.00984v1
• Date: Wed, 2 Jun 2021 07:03:54 GMT
• Title: Few-Shot Partial-Label Learning
• Authors: Yunfeng Zhao, Guoxian Yu, Lei Liu, Zhongmin Yan, Lizhen Cui and Carlotta Domeniconi
• Abstract summary: Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class by training on overly-annotated samples. Existing few-shot learning algorithms assume precise labels of the support set; as such, irrelevant labels may seriously mislead the meta-learner. In this paper, we introduce an approach called FsPLL (Few-shot image learning)
• Score: 25.609766770479265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples for training. However, it is more common than not to have just few PL samples at hand when dealing with new tasks. Furthermore, existing few-shot learning algorithms assume precise labels of the support set; as such, irrelevant labels may seriously mislead the meta-learner and thus lead to a compromised performance. How to enable PLL under a few-shot learning setting is an important problem, but not yet well studied. In this paper, we introduce an approach called FsPLL (Few-shot PLL). FsPLL first performs adaptive distance metric learning by an embedding network and rectifying prototypes on the tasks previously encountered. Next, it calculates the prototype of each class of a new task in the embedding network. An unseen example can then be classified via its distance to each prototype. Experimental results on widely-used few-shot datasets (Omniglot and miniImageNet) demonstrate that our FsPLL can achieve a superior performance than the state-of-the-art methods across different settings, and it needs fewer samples for quickly adapting to new tasks.

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