Revisiting Training Strategies and Generalization Performance in Deep Metric Learning (2002.08473v9)

Abstract: Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year. Although the field benefits from the rapid progress, the divergence in training protocols, architectures, and parameter choices make an unbiased comparison difficult. To provide a consistent reference point, we revisit the most widely used DML objective functions and conduct a study of the crucial parameter choices as well as the commonly neglected mini-batch sampling process. Under consistent comparison, DML objectives show much higher saturation than indicated by literature. Further based on our analysis, we uncover a correlation between the embedding space density and compression to the generalization performance of DML models. Exploiting these insights, we propose a simple, yet effective, training regularization to reliably boost the performance of ranking-based DML models on various standard benchmark datasets. Code and a publicly accessible WandB-repo are available at https://github.com/Confusezius/Revisiting_Deep_Metric_Learning_PyTorch.

Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

This paper provides a systematic analysis of Deep Metric Learning (DML) by evaluating training strategies and generalization capabilities of various DML models. The authors address a significant issue within the DML community: the lack of standardized training protocols that complicates unbiased comparison between different models. By establishing a consistent reference point, this research attempts to quantify the real efficacy of different DML objectives and training parameters.

Objective Analysis and Training Protocols

The researchers revisit popular DML objective functions such as ranking-based and classification-based losses. Their examination includes objectives like triplet losses, Angular loss, and Proxy-based methods, each with distinct mechanisms to measure and optimize embedding spaces to reflect visual similarities. Notably, the paper focuses on aligning these objectives' evaluation metrics under cohesive training settings, revealing a saturation of performance across models that literature may overstate.

For a fair comparison, the paper implements consistent architectures, data preprocessing, and parameter configurations. The findings suggest that disparities in reported performances are often due to differences in underlying setups rather than intrinsic algorithm superiority. The investigation into factors such as batch size, architecture choice, and weight decay further uncovers their impact on model performance, emphasizing the necessity of transparent reporting in future studies.

Data Sampling and Mining Strategies

The paper also explores the data sampling process, which has been relatively neglected in DML literature. By exploring strategies like semi-hard and distance-weighted sampling, as well as innovative approaches like FRD and DDM, the authors highlight the importance of mini-batch composition. They find that diverse data samples within batches generally enhance learning outcomes. This analysis underscores the indirect role of data diversity in facilitating robust gradient updates and better generalization.

Generalization Insights and Compression

An important contribution of this work is the analysis of generalization through embedding space characteristics. It identifies a strong negative correlation between spectral decay—i.e., the compression of singular value spectra—and generalization performance in DML. Unlike classification tasks that benefit from feature compression, DML thrives on preserving multiple directions of significant variance. This insight aligns with the concept of embedding space density, where denser representations support better out-of-distribution generalization.

Regularization of Embedding Spaces

Leveraging the identified correlation between spectral decay and generalization, the paper proposes a $\rho$-regularization technique for improving the performance of ranking-based DML approaches. By mildly perturbing the learning signals through random negative sampling, this regularization balances the compression of embedding spaces, enhancing diversity without sacrificing discriminatory power. Comparative results show a consistent boost in model performance across standard benchmark datasets.

Implications and Future Directions

The findings have significant implications for the development and evaluation of future DML models. The work presents a compelling case for standardized benchmarking practices, which could accelerate advances in the field by focusing on the intrinsic qualities of algorithms rather than variances introduced by inconsistent setups.

Moreover, the paper's insights into generalization could inform new learning paradigms that better balance feature compression and diversity, especially in scenarios characterized by significant domain shifts.

Conclusion

By methodically dissecting the components of DML pipelines, this research contributes valuable clarity to the field by decoding the relationship between training practices, objective functions, and generalization performance. The introduction of $\rho$-regularization enriches the toolkit available for designing more robust DML systems, providing a bridge between current capabilities and the nuanced demands of real-world applications. Future exploration is encouraged to continue refining these methodologies, particularly in extending the findings to unsupervised and semi-supervised DML contexts.