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.

翻译：摘要：ODIN是一种创新方法，通过整合生成式人工智能模型来解决数据集的约束问题。传统的零样本学习方法受限于训练数据集。为从根本上突破这一限制，ODIN尝试通过根据用户需求生成按需数据集来缓解数据集约束。ODIN包含三个主要模块：提示生成器、文本到图像生成器和图像后处理器。为生成高质量提示和图像，我们分别采用了大型语言模型（例如ChatGPT）和文本到图像扩散模型（例如Stable Diffusion）。我们基于模型准确性和数据多样性在各种数据集上评估了ODIN，以展示其潜力，并开展了后续实验进行进一步探究。总体而言，ODIN是一种可行的方法，使人工智能能够学习训练数据集之外的未知知识。