IllusionFashionMNIST_test

IllusionFashionMNIST_test

Dataset Characteristics

IllusionFashionMNIST_test is a generated dataset derived from the FashionMNIST dataset. It incorporates the concept of pareidolia—a phenomenon where patterns, often faces, are perceived in random or abstract stimuli. The dataset contains 11 classes: the original 10 classes from FashionMNIST, and an additional "No Illusion" class. It includes 1,267 samples, all synthetically created rather than real-world images.

Motivations and Content Summary

The dataset was created using ControlNet for image generation, with captions produced by four large language models (LLMs). The aim is to combine the complex visual patterns in fashion items with the phenomenon of pareidolia, encouraging models to reason about visual illusions in the context of non-digit, real-world-like data. This introduces an added layer of abstraction to the already challenging task of image classification.

Potential Use Cases

• Illusory VQA: Questioning models about the illusions present in the images.
• Multimodal Model Evaluation: Benchmarking multimodal models' ability to interpret and reason about abstract and illusory patterns.
• Perceptual Studies: Investigating how AI models perceive pareidolia in fashion-related visuals.
• Synthetic Data Research: Exploring the use of generated datasets to present unconventional challenges to machine learning models.