Deep Metric Learning by Online Soft Mining and Class-Aware Attention

Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learning is known to suffer from slow convergence due to a large fraction of trivial samples. Therefore, most existing methods generally resort to sample mining strategies for selecting nontrivial samples to accelerate convergence and improve performance. In this work, we identify two critical limitations of the sample mining methods, and provide solutions for both of them. First, previous mining methods assign one binary score to each sample, i.e., dropping or keeping it, so they only selects a subset of relevant samples in a mini-batch. Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch. OSM learns extended manifolds that preserve useful intraclass variances by focusing on more similar positives. Second, the existing methods are easily influenced by outliers as they are generally included in the mined subset. To address this, we introduce Class-Aware Attention (CAA) that assigns little attention to abnormal data samples. Furthermore, by combining OSM and CAA, we propose a novel weighted contrastive loss to learn discriminative embeddings. Extensive experiments on two fine-grained visual categorisation datasets and two video-based person re-identification benchmarks show that our method significantly outperforms the state-of-the-art.

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Fine-grained Patient Similarity Measuring using Deep Metric Learning

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17 ◽

10.1145/3132847.3133022 ◽

2017 ◽

Cited By ~ 6

Author(s):

Jiazhi Ni ◽

Jie Liu ◽

Chenxin Zhang ◽

Dan Ye ◽

Zhirou Ma

Keyword(s):

Metric Learning ◽

Fine Grained ◽

Deep Metric Learning ◽

Similarity Measuring

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Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ◽

10.1109/cvpr.2016.130 ◽

2016 ◽

Cited By ~ 66

Author(s):

Yin Cui ◽

Feng Zhou ◽

Yuanqing Lin ◽

Serge Belongie

Keyword(s):

Metric Learning ◽

Fine Grained ◽

Deep Metric Learning

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Joint Learning of the Center Points and Deep Metrics for Land-Use Classification in Remote Sensing

Remote Sensing ◽

10.3390/rs11010076 ◽

2019 ◽

Vol 11 (1) ◽

pp. 76 ◽

Cited By ~ 6

Author(s):

Zhiqiang Gong ◽

Ping Zhong ◽

Weidong Hu ◽

Yuming Hua

Keyword(s):

Remote Sensing ◽

Land Use ◽

Metric Learning ◽

Land Use Classification ◽

Training Process ◽

Pairwise Correlation ◽

Deep Model ◽

Training Samples ◽

Deep Metric Learning ◽

Learned Features

Deep learning methods, especially convolutional neural networks (CNNs), have shown remarkable ability for remote sensing scene classification. However, the traditional training process of standard CNNs only takes the point-wise penalization of the training samples into consideration, which usually makes the learned CNNs sub-optimal especially for remote sensing scenes with large intra-class variance and low inter-class variance. To address this problem, deep metric learning, which incorporates the metric learning into the deep model, is used to maximize the inter-class variance and minimize the intra-class variance for better representation. This work introduces structured metric learning for remote sensing scene representation, a special deep metric learning which can take full advantage of the training batch. However, the deep metrics only consider the pairwise correlation between the training samples, and ignores the classwise correlation from the class view. To take the classwise penalization into consideration, this work defines the center points of the learned features of each class in the training process to represent the class. Through increasing the variance between different center points and decreasing the variance between the learned features from each class and the corresponding center point, the representational ability can be further improved. Therefore, this work develops a novel center-based structured metric learning to take advantage of both the deep metrics and the center points. Finally, joint supervision of the cross-entropy loss and the center-based structured metric learning is developed for the land-use classification in remote sensing. It can joint learn the center points and the deep metrics to take advantage of the point-wise, the pairwise, and the classwise correlation. Experiments are conducted over three real-world remote sensing scene datasets, namely UC Merced Land-Use dataset, Brazilian Coffee Scene dataset, and Google dataset. The classification performance can achieve 97.30%, 91.24%, and 92.04% with the proposed method over the three datasets which are better than other state-of-the-art methods under the same experimental setups. The results demonstrate that the proposed method can improve the representational ability for the remote sensing scenes.

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Deep metric attention learning for skin lesion classification in dermoscopy images

Complex & Intelligent Systems ◽

10.1007/s40747-021-00587-4 ◽

2022 ◽

Author(s):

Xiaoyu He ◽

Yong Wang ◽

Shuang Zhao ◽

Chunli Yao

Keyword(s):

Skin Lesion ◽

Metric Learning ◽

Feature Representation ◽

Training Procedure ◽

Training Samples ◽

Triplet Loss ◽

Deep Metric Learning ◽

Localization Ability ◽

Lesion Localization ◽

Lesion Classification

AbstractCurrently, convolutional neural networks (CNNs) have made remarkable achievements in skin lesion classification because of their end-to-end feature representation abilities. However, precise skin lesion classification is still challenging because of the following three issues: (1) insufficient training samples, (2) inter-class similarities and intra-class variations, and (3) lack of the ability to focus on discriminative skin lesion parts. To address these issues, we propose a deep metric attention learning CNN (DeMAL-CNN) for skin lesion classification. In DeMAL-CNN, a triplet-based network (TPN) is first designed based on deep metric learning, which consists of three weight-shared embedding extraction networks. TPN adopts a triplet of samples as input and uses the triplet loss to optimize the embeddings, which can not only increase the number of training samples, but also learn the embeddings robust to inter-class similarities and intra-class variations. In addition, a mixed attention mechanism considering both the spatial-wise and channel-wise attention information is designed and integrated into the construction of each embedding extraction network, which can further strengthen the skin lesion localization ability of DeMAL-CNN. After extracting the embeddings, three weight-shared classification layers are used to generate the final predictions. In the training procedure, we combine the triplet loss with the classification loss as a hybrid loss to train DeMAL-CNN. We compare DeMAL-CNN with the baseline method, attention methods, advanced challenge methods, and state-of-the-art skin lesion classification methods on the ISIC 2016 and ISIC 2017 datasets, and test its generalization ability on the PH2 dataset. The results demonstrate its effectiveness.

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Symmetrical Synthesis for Deep Metric Learning

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6716 ◽

2020 ◽

Vol 34 (07) ◽

pp. 10853-10860

Author(s):

Geonmo Gu ◽

Byungsoo Ko

Keyword(s):

Metric Learning ◽

Generative Adversarial Networks ◽

Learning Loss ◽

Adversarial Networks ◽

Data Points ◽

Novel Method ◽

Deep Metric Learning ◽

Similarity Information ◽

Negative Pair ◽

Original Feature

Deep metric learning aims to learn embeddings that contain semantic similarity information among data points. To learn better embeddings, methods to generate synthetic hard samples have been proposed. Existing methods of synthetic hard sample generation are adopting autoencoders or generative adversarial networks, but this leads to more hyper-parameters, harder optimization, and slower training speed. In this paper, we address these problems by proposing a novel method of synthetic hard sample generation called symmetrical synthesis. Given two original feature points from the same class, the proposed method firstly generates synthetic points with each other as an axis of symmetry. Secondly, it performs hard negative pair mining within the original and synthetic points to select a more informative negative pair for computing the metric learning loss. Our proposed method is hyper-parameter free and plug-and-play for existing metric learning losses without network modification. We demonstrate the superiority of our proposed method over existing methods for a variety of loss functions on clustering and image retrieval tasks.

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