MoDE: CLIP Data Experts via Clustering

Enhanced Training Efficiency in CLIP Models Through Mixture of Data Experts (MoDE)

Introduction

The paper presents a novel framework called Mixture of Data Experts (MoDE), which addresses the challenges in training Contrastive Language-Image Pre-training (CLIP) models. CLIP models generally suffer from the noise inherent in web-crawled data pairs (image-caption), impacting their training effectiveness. MoDE introduces a strategy to mitigate this by employing multiple data experts, each trained on distinct, semantically coherent data clusters. This approach enhances model robustness against false negative samples and improves training efficiency.

Approach

The core methodology of MoDE involves:

• Clustering: Data is divided into fine-grained clusters, ensuring that each cluster maintains semantic coherence. This clustering is crucial as it allows each data expert to specialize, reducing sensitivity to noise in other data subsets.
• Training of Data Experts: Each cluster is linked to a specific data expert model that trains solely on that cluster's data. This separation allows for focused and efficient learning.
• Ensemble During Inference: At inference, outputs from different experts are combined. The weighting of these outputs is adjusted based on the correlation between task-specific metadata and the conditions of each cluster.

This structured approach not only tackles the noise issue but also streamlines the training process by allowing asynchronous training of data experts.

Experimental Results

The experimental evaluation of MoDE reveals several critical findings:

• The MoDE framework, utilizing four CLIP data experts based on the ViT-B/16 architecture, outperforms the larger ViT-L/14 model used in OpenAI's CLIP and OpenCLIP in terms of zero-shot image classification accuracy.
• This performance advantage is achieved with significantly lower training costs, specifically less than 35% of the computational and time resources compared to the baseline models.
• It is also highlighted that the flexibility of the MoDE framework supports the addition of new data experts without requiring a complete retraining of the system.

Implications and Future Work

The MoDE approach significantly enhances the practicality and scalability of CLIP models by addressing key limitations around training efficiency and noise sensitivity. From a theoretical standpoint, the use of semantically coherent clusters and expert-based training could influence future designs of not only image-caption models but broader multimodal architectures.

Speculatively, the framework could be adapted for generative models, potentially offering a pathway to more efficient and scalable generative systems. Such developments could be critical as the demand for sophisticated, resource-efficient AI systems continues to grow.

Conclusion

MoDE represents a strategic evolution in the training of CLIP models, emphasizing efficiency, scalability, and robustness. By effectively utilizing a cluster-based, expert-driven training methodology, it sets a foundation for future advancements in both the practical deployment and theoretical development of generative and discriminative multimodal systems. Moreover, the asynchronous training capability and the potential for future expansion make MoDE an adaptable solution suited to the dynamic nature of AI research and application challenges.