3 papers
cs.AI2025
Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI
Qi Huang, Emanuele Mezzi, Osman Mutlu +5
We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models. We posit that for models to be truly interpretable and trustwor…
cs.LG2024
Progressive Monitoring of Generative Model Training Evolution
Vidya Prasad, Anna Vilanova, Nicola Pezzotti
While deep generative models (DGMs) have gained popularity, their susceptibility to biases and other inefficiencies that lead to undesirable outcomes remains an issue. With their g…
cs.CV2024
EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models
Vidya Prasad, Hans van Gorp, Christina Humer +3
Diffusion models, widely used in image generation, rely on iterative refinement to generate images from noise. Understanding this data evolution is important for model development…