Learning Intra-Batch Connections for Deep Metric Learning (2102.07753v3)

Abstract: The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric learning utilizes neural networks to learn such a mapping. Most approaches rely on losses that only take the relations between pairs or triplets of samples into account, which either belong to the same class or two different classes. However, these methods do not explore the embedding space in its entirety. To this end, we propose an approach based on message passing networks that takes all the relations in a mini-batch into account. We refine embedding vectors by exchanging messages among all samples in a given batch allowing the training process to be aware of its overall structure. Since not all samples are equally important to predict a decision boundary, we use an attention mechanism during message passing to allow samples to weigh the importance of each neighbor accordingly. We achieve state-of-the-art results on clustering and image retrieval on the CUB-200-2011, Cars196, Stanford Online Products, and In-Shop Clothes datasets. To facilitate further research, we make available the code and the models at https://github.com/dvl-tum/intra_batch_connections.

• The paper introduces a novel deep metric learning approach that learns intra-batch connections using message passing networks to improve embedding structure.
• The method refines initial embeddings through graph-based message passing steps with self-attention, dynamically adjusting connections within the mini-batch.
• Numerical results demonstrate state-of-the-art performance on image retrieval and clustering tasks across multiple datasets, showing notable Recall@1 and NMI improvements.

Learning Intra-Batch Connections for Deep Metric Learning

The paper "Learning Intra-Batch Connections for Deep Metric Learning" introduces a novel approach to enhance metric learning through the comprehensive consideration of intra-batch relations. Traditional deep metric learning primarily utilizes pairwise or triplet comparisons to learn embeddings that map samples to a lower-dimensional space. While effective, these methods often overlook the broader embedding space's structure. This paper proposes leveraging message passing networks (MPNs) to refine sample embeddings by accounting for all relations within a mini-batch, thereby improving the overall structure and efficiency of the learned metric space.

Methodology

The authors present a fully learnable model that refines embedding vectors through MPNs, enabling communication between samples in a mini-batch. The method commences with using a convolutional neural network (CNN) to initialize embedding vectors and constructs a fully connected graph where each node represents a sample in the batch. The embedding vectors are progressively refined through a series of message-passing steps, with a self-attention mechanism dictating the importance of neighboring samples. This enables samples to dynamically weigh how much influence each neighboring sample should exert, optimizing the prediction of decision boundaries.

This paper contrasts the proposed approach with existing methods like triplet loss, highlighting that while traditional methods depend on fixed triplets, MPNs can dynamically adjust connections across the entire mini-batch, thus capturing a more global structure.

Numerical Results

The paper reports state-of-the-art results in clustering and image retrieval tasks across several datasets, including CUB-200-2011, Cars196, Stanford Online Products, and In-Shop Clothes. It claims improvements in Recall@1 metrics as well as Normalized Mutual Information (NMI), outperforming existing methods by notable margins. Specifically, the proposed method achieves a Recall@1 improvement of 0.6 percentage points on CUB-200-2011 and a 1.3 percentage points improvement on Stanford Online Products over the previous best-performing methods.

Implications and Future Directions

The practical implications of this research are significant in fields reliant on accurate clustering and retrieval, such as computer vision and pattern recognition. The theoretical contribution furthers understanding in metric learning, particularly in the calculus involving combinatorial relations within data representation spaces. The use of MPNs not only enhances the embedding process but also sets a precedent for integrating graph-based approaches in metric learning.

Looking forward, the authors speculate that similar MPN architectures could be effectively applied to semi-supervised metric learning or scenarios involving relative label availability only. This approach paves the way for further exploration into robust metric learning frameworks that could mitigate traditional dataset structure limitations.

Conclusion

The paper establishes a compelling argument for utilizing intra-batch connections, moving beyond pairwise or triplet methods, to achieve more refined, efficient metric embeddings. By creatively applying message passing principles from graph neural networks, it offers new insights and directions for metric learning research, reinforcing the pivotal role of inter-sample relationships in optimizing deep learning tasks. The authors conclusively demonstrate the potential for improved performance without the need for increased complexity, which stands as a testament to the ingenuity and efficacy of their proposed method.