Generalization in Metric Learning: Should the Embedding Layer Be Embedding Layer?

@article{Vo2018GeneralizationIM,
  title={Generalization in Metric Learning: Should the Embedding Layer Be Embedding Layer?},
  author={Nam S. Vo and James Hays},
  journal={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2018},
  pages={589-598}
}
  • Nam S. VoJames Hays
  • Published 8 March 2018
  • Computer Science
  • 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
This work studies deep metric learning under small to medium scale as we believe that better generalization could be a contributing factor to the improvement of previous fine-grained image retrieval methods; it should be considered when designing future techniques. In particular, we investigate using other layers in a deep metric learning system (besides the embedding layer) for feature extraction and analyze how well they perform on training data and generalize to testing data. From this study… 
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27 Citations
Highly Influential Citations
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Background Citations
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27 Citations

The Group Loss for Deep Metric Learning

Group Loss is proposed, 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.

The Group Loss++: A Deeper Look Into Group Loss for Deep Metric Learning

This work proposes 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.

Dynamically Modulated Deep Metric Learning for Visual Search

This paper proposes a new soft-binomial deviance loss that helps to capture the feature similarity space at multiple granularities and achieves state-of-the-art performances on standard benchmark datasets.

Deep Metric Learning via Adaptive Learnable Assessment

A sequence-aware learnable assessor is proposed which re-weights each training example to train the metric towards good generalization and demonstrates the effectiveness of the proposed approach on widely used CUB-200-2011, Cars196, and Stanford Online Products datasets.

Semantic Granularity Metric Learning for Visual Search

Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling

This paper proposes learning an embedding function for content-based image retrieval within the e-commerce domain using the triplet loss and an online sampling method that constructs triplets from within a minibatch that significantly outperforms the state-of-the-art on the DeepFashion dataset.

Visualizing Deep Similarity Networks

The visualization shows how similarity networks that are fine-tuned learn to focus on different features and generalize the approach to embedding networks that use different pooling strategies and provide a simple mechanism to support image similarity searches on objects or sub-regions in the query image.

Adapting Grad-CAM for Embedding Networks

This work proposes an adaptation of the Grad-CAM method for embedding networks, and develops an efficient weight-transfer method to explain decisions for any image without back-propagation.

Visualizing How Embeddings Generalize

In observing many triplet selection strategies for Metric Learning, it is found that the best performance consistently arises from approaches that focus on a few, well selected triplets.

Stochastic Class-Based Hard Example Mining for Deep Metric Learning

The key idea is to adopt class signatures that keep track of feature embedding online with minor additional cost during training, and identify hard negative example candidates using the signatures and achieves the state-of-the-art performance on the several standard benchmark datasets.

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