GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training

TL;DR

GrowCLIP introduces a data-driven, automatic model growing approach for contrastive language-image pre-training that adapts architecture dynamically to continuously growing data, enhancing performance on downstream tasks.

Contribution

It proposes a novel dynamic growth space and parameter inheriting method, enabling models to adapt architecture during online learning for improved efficiency and accuracy.

Findings

Achieves 2.3% higher top-1 accuracy on zero-shot classification

Improves 1.2% top-1 image-to-text recall on Flickr30K

Demonstrates effective adaptation to continuously growing data

Abstract

Cross-modal pre-training has shown impressive performance on a wide range of downstream tasks, benefiting from massive image-text pairs collected from the Internet. In practice, online data are growing constantly, highlighting the importance of the ability of pre-trained model to learn from data that is continuously growing. Existing works on cross-modal pre-training mainly focus on training a network with fixed architecture. However, it is impractical to limit the model capacity when considering the continuously growing nature of pre-training data in real-world applications. On the other hand, it is important to utilize the knowledge in the current model to obtain efficient training and better performance. To address the above issues, in this paper, we propose GrowCLIP, a data-driven automatic model growing algorithm for contrastive language-image pre-training with continuous image-text pairs as input. Specially, we adopt a dynamic growth space and seek out the optimal architecture at each growth step to adapt to online learning scenarios. And the shared encoder is proposed in our growth space to enhance the degree of cross-modal fusion. Besides, we explore the effect of growth in different dimensions, which could provide future references for the design of cross-modal model architecture. Finally, we employ parameter inheriting with momentum (PIM) to maintain the previous knowledge and address the issue of the local minimum dilemma. Compared with the existing methods, GrowCLIP improves 2.3% average top-1 accuracy on zero-shot image classification of 9 downstream tasks. As for zero-shot image retrieval, GrowCLIP can improve 1.2% for top-1 image-to-text recall on Flickr30K dataset.