Explaining generative diffusion models via visual analysis for interpretable decision-making process

Author:

Park Ji-HoonORCID,Ju Yeong-JoonORCID,Lee Seong-WhanORCID

Funder

Institute for Information Communication Technology Planning and Evaluation

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference44 articles.

1. Avrahami, O., Lischinski, D., & Fried, O. (2022). Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 18208–18218).

2. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation;Bach;PLoS One,2015

3. Unleashing transformers: Parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes;Bond-Taylor,2022

4. High fidelity visualization of what your self-supervised representation knows about;Bordes,2022

5. Fault diagnosis using explainable AI: A transfer learning-based approach for rotating machinery exploiting augmented synthetic data;Brito,2023

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