Proceedings ArticleDOI
BIER — Boosting Independent Embeddings Robustly
Michael Opitz,Georg Waltner,Horst Possegger,Horst Bischof +3 more
- pp 5199-5208
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TLDR
The metric learning method improves over state-ofthe- art methods on image retrieval methods on the CUB-200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets by a significant margin.Abstract:
Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. This leverages large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increases retrieval accuracy of the embedding. Our method does not introduce any additional parameters and works with any differentiable loss function. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-ofthe- art methods on the CUB-200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets by a significant margin.read more
Citations
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Book ChapterDOI
Deep Metric Learning with Hierarchical Triplet Loss
TL;DR: Huang et al. as mentioned in this paper proposed a hierarchical triplet loss (HTL) to automatically collect informative training samples via a defined hierarchical tree that encodes global context information, which allows the model to learn more discriminative features from visual similar classes, leading to faster convergence and better performance.
Proceedings ArticleDOI
Ranked List Loss for Deep Metric Learning
TL;DR: This work presents two limitations of existing ranking-motivated structured losses and proposes a novel ranked list loss to solve both of them and proposes to learn a hypersphere for each class in order to preserve the similarity structure inside it.
Proceedings ArticleDOI
Deep Adversarial Metric Learning
TL;DR: This paper proposes a deep adversarial metric learning (DAML) framework to generate synthetic hard negatives from the observed negative samples, which is widely applicable to supervised deep metric learning methods.
Proceedings ArticleDOI
Cross-Batch Memory for Embedding Learning
TL;DR: This paper proposes a cross-batch memory (XBM) mechanism that memorizes the embeddings of past iterations, allowing the model to collect sufficient hard negative pairs across multiple mini-batches - even over the whole dataset.
Book ChapterDOI
Attention-Based Ensemble for Deep Metric Learning
TL;DR: Zhang et al. as discussed by the authors proposed an attention-based ensemble, which uses multiple attention masks, so that each learner can attend to different parts of the object, and also proposed a divergence loss, which encourages diversity among the learners.
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