تقرير
Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification
| العنوان: | Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification |
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| المؤلفون: | Thammasorn, Phawis, Hippe, Daniel, Chaovalitwongse, Wanpracha, Spraker, Matthew, Wootton, Landon, Nyflot, Matthew, Combs, Stephanie, Peeken, Jan, Ford, Eric |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | Deep representation learning using triplet network for classification suffers from a lack of theoretical foundation and difficulty in tuning both the network and classifiers for performance. To address the problem, local-margin triplet loss along with local positive and negative mining strategy is proposed with theory on how the strategy integrate nearest-neighbor hyper-parameter with triplet learning to increase subsequent classification performance. Results in experiments with 2 public datasets, MNIST and Cifar-10, and 2 small medical image datasets demonstrate that proposed strategy outperforms end-to-end softmax and typical triplet loss in settings without data augmentation while maintaining utility of transferable feature for related tasks. The method serves as a good performance baseline where end-to-end methods encounter difficulties such as small sample data with limited allowable data augmentation. Comment: Triplet Network, Representation Learning, Transfer Learning, Nearest Neighbor, Medical Image Classification |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1911.07940 |
| رقم الأكسشن: | edsarx.1911.07940 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |