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:为了从根本上克服这一限制,ODIN试图通过根据用户要求生成点需求数据集来减轻数据集的限制因素。ODIN由三个主要模块组成:即时生成器、文本到图像生成器和图像处理器。为了产生高质量的提示和图像,我们采用了一个大型语言模型(例如查特格普特)和一个文本到图像传播模型(例如Stable Difmulation)。我们从模型准确性和数据多样性的角度对各种数据集进行了评估,以展示其潜力,并进行了实验后进一步调查。总体而言,ODIN是一种可行的方法,使Al能够在培训数据集之外学习看不见的知识。</s>