Transductive Maximum Margin Classifier for Few-Shot Learning
- URL: http://arxiv.org/abs/2107.11975v1
- Date: Mon, 26 Jul 2021 06:02:32 GMT
- Title: Transductive Maximum Margin Classifier for Few-Shot Learning
- Authors: Fei Pan, Chunlei Xu, Jie Guo, Yanwen Guo
- Abstract summary: Few-shot learning aims to train a classifier that can generalize well when just a small number of labeled samples per class are given.
We introduce Transductive Maximum Margin (TMMC) for corresponding few-shot learning.
TMMC is constructed using a mixture of the labeled support set and the unlabeled query set in a given task.
- Score: 17.18278071760926
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Few-shot learning aims to train a classifier that can generalize well when
just a small number of labeled samples per class are given. We introduce
Transductive Maximum Margin Classifier (TMMC) for few-shot learning. The basic
idea of the classical maximum margin classifier is to solve an optimal
prediction function that the corresponding separating hyperplane can correctly
divide the training data and the resulting classifier has the largest geometric
margin. In few-shot learning scenarios, the training samples are scarce, not
enough to find a separating hyperplane with good generalization ability on
unseen data. TMMC is constructed using a mixture of the labeled support set and
the unlabeled query set in a given task. The unlabeled samples in the query set
can adjust the separating hyperplane so that the prediction function is optimal
on both the labeled and unlabeled samples. Furthermore, we leverage an
efficient and effective quasi-Newton algorithm, the L-BFGS method to optimize
TMMC. Experimental results on three standard few-shot learning benchmarks
including miniImagenet, tieredImagenet and CUB suggest that our TMMC achieves
state-of-the-art accuracies.
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