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arXiv · 2303.06832

ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in

SP Choi·Jihun Lee·Hyeongseok Ahn·Sanghee Jung·Bumsoo Kang
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Abstract

ODIN is an innovative approach that addresses the problem of dataset constraints by integrating generative AI models. Traditional zero-shot learning methods are constrained by the training dataset. To fundamentally overcome this limitation, ODIN attempts to mitigate the dataset constraints by generating on-demand datasets based on user requirements. ODIN consists of three main modules: a prompt generator, a text-to-image generator, and an image post-processor. To generate high-quality prompts and images, we adopted a large language model (e.g., ChatGPT), and a text-to-image diffusion model (e.g., Stable Diffusion), respectively. We evaluated ODIN on various datasets in terms of model accuracy and data diversity to demonstrate its potential, and conducted post-experiments for further investigation. Overall, ODIN is a feasible approach that enables Al to learn unseen knowledge beyond the training dataset.

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SP Choi, Jihun Lee, Hyeongseok Ahn, Sanghee Jung, Bumsoo Kang. 2023-03-13. ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in. https://arxiv.org/abs/2303.06832

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