Rank-N-Contrast: Learning Continuous Representations for Regression (2210.01189v2)

Abstract: Deep regression models typically learn in an end-to-end fashion without explicitly emphasizing a regression-aware representation. Consequently, the learned representations exhibit fragmentation and fail to capture the continuous nature of sample orders, inducing suboptimal results across a wide range of regression tasks. To fill the gap, we propose Rank-N-Contrast (RNC), a framework that learns continuous representations for regression by contrasting samples against each other based on their rankings in the target space. We demonstrate, theoretically and empirically, that RNC guarantees the desired order of learned representations in accordance with the target orders, enjoying not only better performance but also significantly improved robustness, efficiency, and generalization. Extensive experiments using five real-world regression datasets that span computer vision, human-computer interaction, and healthcare verify that RNC achieves state-of-the-art performance, highlighting its intriguing properties including better data efficiency, robustness to spurious targets and data corruptions, and generalization to distribution shifts. Code is available at: https://github.com/kaiwenzha/Rank-N-Contrast.

• The paper presents the Rank-n-Contrast framework, which aligns learned representations with the inherent order of continuous labels.
• It offers theoretical guarantees through δ-ordered embeddings that ensure feature ordering matches regression targets.
• Empirical results on datasets like AgeDB and MPIIFaceGaze demonstrate enhanced robustness, generalization, and zero-shot transfer over traditional methods.

An Examination of "Rank-n-Contrast: Learning Continuous Representations for Regression"

In the field of deep learning, regression models are frequently tasked with learning to predict continuous outcomes directly from complex input data. However, traditional deep regression techniques often overlook the intrinsic ordering present in continuous labels, leading to suboptimal learning outcomes. The paper "Rank-n-Contrast: Learning Continuous Representations for Regression" addresses this limitation by introducing a new learning framework, Rank-n-Contrast (RnC), which emphasizes the learning of continuous representations aligned with target rankings to enhance regression tasks.

Summary of Contributions

The paper advances several key contributions:

1. Rank-n-Contrast Framework: RnC is conceptualized to create representation spaces where the intrinsic order present in regression tasks is preserved. This novel framework contrasts samples based on their rankings in the target space, accompanied by a loss function specifically designed to maintain these rankings in the learned feature domain.
2. Theoretical Guarantees: By introducing the concept of $\delta$-ordered feature embeddings, the authors provide robust theoretical justification, proving that optimizing the Rank-n-Contrast loss results in feature embeddings ordered in accordance to continuous labels. This facilitates improvement in performance metrics and generalization capabilities.
3. Empirical Validation: The paper's experimental results, based on diverse datasets such as AgeDB, MPIIFaceGaze, and SkyFinder, substantiate the superior performance of RnC over both standard and state-of-the-art regression techniques. The detailed results emphasize RnC's robustness and efficiency in scenarios such as distribution shifts, data imbalances, and reduced training samples.

Key Results and Implications

Improved Representation Learning: RnC was shown to outperform existing regression techniques in capturing ordered relationships between samples, using the ranking-based loss to enhance generalization across unseen datasets. This represents a pivotal shift in how continuous labels can be leveraged to enrich feature spaces in regression.

Robustness and Efficiency: RnC demonstrated marked resilience to spurious correlations and data corruptions, a notable improvement over previous methods. This robustness is crucial for real-world application where dataset quality and distribution cannot always be controlled.

Transferability and Zero-Shot Generalization: The research highlights how RnC facilitates transfer learning and zero-shot learning, with clear indications that it effectively handles unseen targets and enhances performance when applied to new datasets with limited samples.

Discussion and Future Directions

The methodological advancement proposed by this paper is significant. By successfully integrating representation learning with the natural continuity in regression targets, RnC represents a promising development for applications requiring nuanced understanding of ordered data, including health diagnostics, human-computer interaction, and computer vision.

Future research could expand on integrating RnC in unsupervised or partially labeled scenarios, exploring its adaptability and potential in semi-supervised learning environments. Further investigation into the ethical implications, particularly the interpretability of learned representations and fairness across demographic segments, would also be beneficial, ensuring equitable deployment across diverse application domains.

In conclusion, "Rank-n-Contrast: Learning Continuous Representations for Regression" provides a comprehensive framework that bridges the gap between traditional regression methods and modern representation learning paradigms. It lays a foundation for more robust, efficient, and generalizable approaches to real-world regression challenges, signaling a significant step forward in the pursuit of improved deep learning methodologies.