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Poster
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Xinzhe Li · Qianru Sun · Yaoyao Liu · Qin Zhou · Shibao Zheng · Tat-Seng Chua · Bernt Schiele

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #21

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning. We additionally learn a soft weighting network (SWN) to optimize the self-training weights of pseudo labels so that better ones can contribute more to gradient descent optimization. We evaluate our LST method on two ImageNet benchmarks for semi-supervised few-shot classification and achieve large improvements over the state-of-the-art.

Author Information

Xinzhe Li (SJTU)
Qianru Sun (Singapore Management University)
Yaoyao Liu (Tianjin University)
Qin Zhou (Alibaba Group)
Shibao Zheng (SJTU)
Tat-Seng Chua (National Univ. of Singapore)
Bernt Schiele (Max Planck Institute for Informatics)

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