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ODIN is an innovative approach that addresses the problem of datasetconstraints by integrating generative AI models. Traditional zero-shot learningmethods are constrained by the training dataset. To fundamentally overcome thislimitation, ODIN attempts to mitigate the dataset constraints by generatingon-demand datasets based on user requirements. ODIN consists of three mainmodules: a prompt generator, a text-to-image generator, and an imagepost-processor. To generate high-quality prompts and images, we adopted a largelanguage model (e.g., ChatGPT), and a text-to-image diffusion model (e.g.,Stable Diffusion), respectively. We evaluated ODIN on various datasets in termsof model accuracy and data diversity to demonstrate its potential, andconducted post-experiments for further investigation. Overall, ODIN is afeasible approach that enables Al to learn unseen knowledge beyond the trainingdataset.

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