Related papers: SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

URL: http://arxiv.org/abs/2402.01832v2
Date: Thu, 18 Jul 2024 10:21:29 GMT
Title: SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
Authors: Hasan Abed Al Kader Hammoud, Hani Itani, Fabio Pizzati, Philip Torr, Adel Bibi, Bernard Ghanem,
Abstract summary: We present SynthCLIP, a CLIP model trained on entirely synthetic text-image pairs. We generate synthetic datasets of images and corresponding captions at scale, with no human intervention.
Score: 57.42016037768947
License: http://creativecommons.org/licenses/by/4.0/
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 code, trained models, and data, are released as open source at https://github.com/hammoudhasan/SynthCLIP

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