Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning

• URL: http://arxiv.org/abs/2110.09374v1
• Date: Mon, 18 Oct 2021 14:58:36 GMT
• Title: Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning
• Authors: Uche Osahor, Nasser M. Nasrabadi
• Abstract summary: In few-shot classification, the primary goal is to learn representations that generalize well for novel classes. We propose an efficient low displacement rank (LDR) regularization strategy termed Ortho-Shot.
• Score: 23.465747123791772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In few-shot classification, the primary goal is to learn representations from a few samples that generalize well for novel classes. In this paper, we propose an efficient low displacement rank (LDR) regularization strategy termed Ortho-Shot; a technique that imposes orthogonal regularization on the convolutional layers of a few-shot classifier, which is based on the doubly-block toeplitz (DBT) matrix structure. The regularized convolutional layers of the few-shot classifier enhances model generalization and intra-class feature embeddings that are crucial for few-shot learning. Overfitting is a typical issue for few-shot models, the lack of data diversity inhibits proper model inference which weakens the classification accuracy of few-shot learners to novel classes. In this regard, we broke down the pipeline of the few-shot classifier and established that the support, query and task data augmentation collectively alleviates overfitting in networks. With compelling results, we demonstrated that combining a DBT-based low-rank orthogonal regularizer with data augmentation strategies, significantly boosts the performance of a few-shot classifier. We perform our experiments on the miniImagenet, CIFAR-FS and Stanford datasets with performance values of about 5\% when compared to state-of-the-art

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