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.

ODIN采用生成式AI模型，解决了基于数据集的零样本学习模型的固有限制问题。它主要包括三个模块：提示生成器，文本转图像生成器和图像后处理器。使用大型语言模型和文本到图像扩散模型生成高质量的提示和图像，进一步提高了模型的准确性和数据多样性，并证明了ODIN能够使AI在训练数据之外学习到未知知识。

ODIN：应需数据制定，缓解数据集束缚