Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework (2204.13207v1)

Abstract: Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. We introduce novel hierarchy preserving losses, which jointly apply a hierarchical penalty to the contrastive loss, and enforce the hierarchy constraint. The loss function is data driven and automatically adapts to arbitrary multi-label structures. Experiments on several datasets show that our relationship-preserving embedding performs well on a variety of tasks and outperform the baseline supervised and self-supervised approaches. Code is available at https://github.com/salesforce/hierarchicalContrastiveLearning.

• The paper introduces a novel hierarchical multi-label contrastive framework that utilizes all supervisory signals to enhance representation learning.
• The paper proposes HiMulCon and HiConE loss functions that enforce hierarchical relationships and boost learning efficiency through optimized batch sampling.
• Experimental results on datasets like ImageNet and iNaturalist demonstrate superior performance in classification, retrieval, and clustering over traditional methods.

Overview of "Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework"

The paper presents a novel approach to contrastive learning, introducing a hierarchical multi-label contrastive learning framework designed to utilize all available supervisory signals. It innovatively addresses the hierarchical relationship between classes, ensuring that learned representations can generalize robustly across unseen data and downstream tasks. This is achieved through the development of hierarchy preserving losses, specifically designed to adapt to complex multi-label structures.

Key Contributions

1. Hierarchical Multi-Label Contrastive Learning:
   • Traditional contrastive learning frameworks typically focus on single-label scenarios. This paper presents a contrastive learning model that incorporates all available labels in a hierarchical structure. Recognizing the prevalent occurrence of hierarchical labels in real-world scenarios, such as taxonomy in biology or product categories in e-commerce, this approach effectively captures the nuanced relationships between class labels.
2. Novel Loss Functions:
   • The Hierarchical Multi-label Contrastive Loss (HiMulCon) and the Hierarchical Constraint Enforcing Loss (HiConE) are introduced. HiMulCon applies penalties based on the proximity in the label hierarchy, while HiConE enforces constraints to maintain hierarchy coherence in the learned representation space. The combination of these losses (HiMulConE) integrates hierarchy constraints with contrastive loss penalties, ensuring that representations maintain the inherent label relationships.
3. Sampling Strategy:
   • Designed to guarantee representation at all hierarchy levels in each batch, the proposed hierarchical batch sampling strategy optimizes the formation of positive and negative pairs, enhancing learning efficiency.

Experimental Evaluation

The framework's implementation was tested on several datasets, including ImageNet, DeepFashion, iNaturalist, and ModelNet40. The experimental results highlight the framework's superior performance over baseline approaches such as SimCLR and Supervised Contrastive Learning (SupCon) in classification, retrieval, and clustering tasks. Key numerical results include higher top-1 classification accuracy and improved retrieval metrics, demonstrating the model's ability to leverage hierarchical relationships for enhanced performance.

Implications and Future Directions

The proposed framework enhances the theoretical understanding of contrastive learning by extending it to hierarchical multi-label scenarios. Practically, it provides a versatile method capable of adapting to various structures beyond simple label lists, potentially improving adaptability across diverse domains. The potential for this approach to be expanded into non-hierarchical multi-label settings and other modalities such as audio and text represents an exciting avenue for future research.

Conclusion

By incorporating hierarchical relationships into the contrastive learning framework, the paper presents a significant step in improving generalization and performance on unseen tasks. While the necessity for extensive label information might be a limitation, the model's promising results offer substantial potential for further exploration in multi-modal and more generalized multi-label frameworks. This work contributes to a deeper understanding and more robust application of contrastive learning techniques in complex real-world scenarios.