Exploring Concept Drift Visualization and Explanation in Image Streams

In the rapidly evolving field of Machine Learning applied to data streams, where information arrives continuously and models must adapt in real-time, drift detection has emerged as a critical component for maintaining model accuracy and reliability. While much attention has been given to structured data, the challenge of handling real-time image streams remains comparatively underexplored, despite its vital role in numerous applications. This study addresses this gap by exploring innovative approaches to visualize and explain drift in image streams. We propose a novel method that leverages zero-shot classification capabilities of a pre-trained ResNet-50 architecture to visualize potential changes within the stream. Additionally, we investigate the application of ResNet-50 in combination with Uniform Manifold Approximation and Projection. This hybrid approach aims to identify and visualize drift in high-dimensional image data by projecting it onto a lower-dimensional space, facilitating easier interpretation and analysis of evolving patterns over time. Preliminary results on the CLEAR10 dataset, the first benchmark with a natural temporal evolution of visual concepts, showcase potential applications for drift monitoring in image streams. These findings present a promising avenue for further research and development, aiming at solidifying the integration of these methodologies for improved drift detection and overall models performance in dynamic data contexts.