Using Knowledge Graphs to harvest datasets for efficient CLIP model training
This addresses the challenge of high training costs and data scarcity for domain-specific CLIP models, particularly in scientific research, though it is incremental as it builds on existing CLIP methods.
The paper tackles the problem of high data requirements for training CLIP models by using knowledge graphs to harvest datasets, enabling training of a robust CLIP model from scratch with less data, such as building an expert foundation model for living organisms with just 10M images and introducing EntityNet with 33M images and 46M text descriptions to reduce training time.
Training high-quality CLIP models typically requires enormous datasets, which limits the development of domain-specific models -- especially in areas that even the largest CLIP models do not cover well -- and drives up training costs. This poses challenges for scientific research that needs fine-grained control over the training procedure of CLIP models. In this work, we show that by employing smart web search strategies enhanced with knowledge graphs, a robust CLIP model can be trained from scratch with considerably less data. Specifically, we demonstrate that an expert foundation model for living organisms can be built using just 10M images. Moreover, we introduce EntityNet, a dataset comprising 33M images paired with 46M text descriptions, which enables the training of a generic CLIP model in significantly reduced time.