1. The Group Loss for Deep Metric Learning
- Author
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Marcello Pelillo, Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, and Laura Leal-Taixé
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Similarity (geometry) ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Machine Learning (stat.ML) ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Machine Learning (cs.LG) ,Discriminative model ,Statistics - Machine Learning ,0202 electrical engineering, electronic engineering, information engineering ,Cluster analysis ,Image retrieval ,0105 earth and related environmental sciences ,Artificial neural network ,Settore INF/01 - Informatica ,business.industry ,Pattern recognition ,Deep metric learning ,Image clustering ,Feature (computer vision) ,Metric (mathematics) ,Embedding ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni - Abstract
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes. Much research has been devoted to the design of smart loss functions or data mining strategies for training such networks. Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function, which is commonly based on the distance between embeddings. We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups. Guided by the smoothness assumption that "similar objects should belong to the same group", the proposed loss trains the neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We show state-of-the-art results on clustering and image retrieval on several datasets, and show the potential of our method when combined with other techniques such as ensembles, Accepted to European Conference on Computer Vision (ECCV) 2020, includes non-archival supplementary material
- Published
- 2020