SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
Keywords: CLIP, synthetic data, generative
TL;DR: We train CLIP on 30 million synthetic captions and images and draw insights.
Abstract: We present SynthCLIP, a CLIP model trained on entirely synthetic text-image pairs. Leveraging recent text-to-image (TTI) networks and large language models (LLM), we generate synthetic datasets of images and corresponding captions at scale, with no human intervention. In this work, we provide an analysis on CLIP models trained on synthetic data. We provide insights on the data generation strategy, number of samples required, scaling trends, and resulting properties. We also introduce SynthCI-30M, a purely synthetic dataset comprising 30 million captioned images. Our work focuses on showing the advantages and disadvantages of synthetic data for training CLIP models. Our code, trained models, and data, will be released as open source.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 6345
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