TY - JOUR A1 - Mokbel, Bassam A1 - Paaßen, Benjamin A1 - Schleif, Frank-Michael A1 - Hammer, Barbara T1 - Metric learning for sequences in relational LVQ JF - Neurocomputing Y1 - 2015 U6 - https://doi.org/10.1016/j.neucom.2014.11.082 VL - 169 SP - 306 EP - 322 ER - TY - JOUR A1 - Hofmann, Daniela A1 - Schleif, Frank-Michael A1 - Paaßen, Benjamin A1 - Hammer, Barbara T1 - Learning interpretable kernelized prototype-based models JF - Neurocomputing Y1 - 2014 U6 - https://doi.org/10.1016/j.neucom.2014.03.003 VL - 141 SP - 84 EP - 96 ER - TY - JOUR A1 - Väth, Philipp A1 - Frühwald, Alexander M. A1 - Paaßen, Benjamin A1 - Gregorová, Magda T1 - Diffusion-based Visual Counterfactual Explanations - Towards Systematic Quantitative Evaluation JF - CoRR N2 - Latest methods for visual counterfactual explanations (VCE) harness the power of deep generative models to synthesize new examples of high-dimensional images of impressive quality. However, it is currently difficult to compare the performance of these VCE methods as the evaluation procedures largely vary and often boil down to visual inspection of individual examples and small scale user studies. In this work, we propose a framework for systematic, quantitative evaluation of the VCE methods and a minimal set of metrics to be used. We use this framework to explore the effects of certain crucial design choices in the latest diffusion-based generative models for VCEs of natural image classification (ImageNet). We conduct a battery of ablation-like experiments, generating thousands of VCEs for a suite of classifiers of various complexity, accuracy and robustness. Our findings suggest multiple directions for future advancements and improvements of VCE methods. By sharing our methodology and our approach to tackle the computational challenges of such a study on a limited hardware setup (including the complete code base), we offer a valuable guidance for researchers in the field fostering consistency and transparency in the assessment of counterfactual explanations. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2308.06100 VL - abs/2308.06100 ER -