Li Liang, Jin Weidong, Huang Yingkun
Southwest Jiaotong University, Chengdu City, Sichuan Province China.
China-ASEAN International Joint Laboratory of Integrated Transportation, Nanning University, Nanning City, Guangxi Province China.
Appl Intell (Dordr). 2022;52(6):6148-6163. doi: 10.1007/s10489-021-02769-6. Epub 2021 Sep 2.
This paper presents a novel discriminative Few-shot learning architecture based on batch compact loss. Currently, Convolutional Neural Network (CNN) has achieved reasonably good performance in image recognition. Most existing CNN methods facilitate classifiers to learn discriminating patterns to identify existing categories trained with large samples. However, learning to recognize novel categories from a few examples is a challenging task. To address this, we propose the Residual Compact Network to train a deep neural network to learn hierarchical nonlinear transformations to project image pairs into the same latent feature space, under which the distance of each positive pair is reduced. To better use the commonality of class-level features for category recognition, we develop a batch compact loss to form robust feature representations relevant to a category. The proposed methods are evaluated on several datasets. Experimental evaluations show that our proposed method achieves acceptable results in Few-shot learning.
本文提出了一种基于批紧致损失的新型判别式少样本学习架构。目前，卷积神经网络（CNN）在图像识别方面取得了相当不错的性能。大多数现有的CNN方法帮助分类器学习判别模式，以识别用大量样本训练的现有类别。然而，从少数示例中学习识别新类别是一项具有挑战性的任务。为了解决这个问题，我们提出了残差紧致网络来训练深度神经网络，以学习分层非线性变换，将图像对投影到相同的潜在特征空间中，在该空间下每个正样本对的距离会减小。为了更好地利用类别级特征的共性进行类别识别，我们开发了一种批紧致损失，以形成与一个类别相关的鲁棒特征表示。所提出的方法在几个数据集上进行了评估。实验评估表明，我们提出的方法在少样本学习中取得了可接受的结果。
