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Diego Velazquez; Pau Rodriguez; Alexandre Lacoste; Issam H. Laradji; Xavier Roca; Jordi Gonzalez |
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Title |
Evaluating Counterfactual Explainers |
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2023 |
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Transactions on Machine Learning Research |
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TMLR |
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Explainability; Counterfactuals; XAI |
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Explainability methods have been widely used to provide insight into the decisions made by statistical models, thus facilitating their adoption in various domains within the industry. Counterfactual explanation methods aim to improve our understanding of a model by perturbing samples in a way that would alter its response in an unexpected manner. This information is helpful for users and for machine learning practitioners to understand and improve their models. Given the value provided by counterfactual explanations, there is a growing interest in the research community to investigate and propose new methods. However, we identify two issues that could hinder the progress in this field. (1) Existing metrics do not accurately reflect the value of an explainability method for the users. (2) Comparisons between methods are usually performed with datasets like CelebA, where images are annotated with attributes that do not fully describe them and with subjective attributes such as ``Attractive''. In this work, we address these problems by proposing an evaluation method with a principled metric to evaluate and compare different counterfactual explanation methods. The evaluation method is based on a synthetic dataset where images are fully described by their annotated attributes. As a result, we are able to perform a fair comparison of multiple explainability methods in the recent literature, obtaining insights about their performance. We make the code public for the benefit of the research community. |
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Admin @ si @ VRL2023 |
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3891 |
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Author |
Ignasi Rius; Jordi Gonzalez; Mikhail Mozerov; Xavier Roca |
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Title |
Automatic Learning of 3D Pose Variability in Walking Performances for Gait Analysis |
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2008 |
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International Journal for Computational Vision and Biomechanics |
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1 |
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1 |
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33–43 |
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ISE @ ise @ RGM2008 |
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1020 |
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Author |
A. Toet; M. Henselmans; M.P. Lucassen; Theo Gevers |
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Title |
Emotional effects of dynamic textures |
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2011 |
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i-Perception |
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iPER |
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2 |
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9 |
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969 – 991 |
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This study explores the effects of various spatiotemporal dynamic texture characteristics on human emotions. The emotional experience of auditory (eg, music) and haptic repetitive patterns has been studied extensively. In contrast, the emotional experience of visual dynamic textures is still largely unknown, despite their natural ubiquity and increasing use in digital media. Participants watched a set of dynamic textures, representing either water or various different media, and self-reported their emotional experience. Motion complexity was found to have mildly relaxing and nondominant effects. In contrast, motion change complexity was found to be arousing and dominant. The speed of dynamics had arousing, dominant, and unpleasant effects. The amplitude of dynamics was also regarded as unpleasant. The regularity of the dynamics over the textures’ area was found to be uninteresting, nondominant, mildly relaxing, and mildly pleasant. The spatial scale of the dynamics had an unpleasant, arousing, and dominant effect, which was larger for textures with diverse content than for water textures. For water textures, the effects of spatial contrast were arousing, dominant, interesting, and mildly unpleasant. None of these effects were observed for textures of diverse content. The current findings are relevant for the design and synthesis of affective multimedia content and for affective scene indexing and retrieval. |
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2041-6695 |
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ALTRES;ISE |
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Admin @ si @THL2011 |
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1843 |
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Albert Ali Salah; Theo Gevers; Nicu Sebe; Alessandro Vinciarelli |
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Computer Vision for Ambient Intelligence |
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2011 |
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Journal of Ambient Intelligence and Smart Environments |
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JAISE |
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3 |
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3 |
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187-191 |
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Admin @ si @ SGS2011a |
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1725 |
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Hamdi Dibeklioglu; M.O. Hortas; I. Kosunen; P. Zuzánek; Albert Ali Salah; Theo Gevers |
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Design and implementation of an affect-responsive interactive photo frame |
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2011 |
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Journal on Multimodal User Interfaces |
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JMUI |
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4 |
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2 |
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81-95 |
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This paper describes an affect-responsive interactive photo-frame application that offers its user a different experience with every use. It relies on visual analysis of activity levels and facial expressions of its users to select responses from a database of short video segments. This ever-growing database is automatically prepared by an offline analysis of user-uploaded videos. The resulting system matches its user’s affect along dimensions of valence and arousal, and gradually adapts its response to each specific user. In an extended mode, two such systems are coupled and feed each other with visual content. The strengths and weaknesses of the system are assessed through a usability study, where a Wizard-of-Oz response logic is contrasted with the fully automatic system that uses affective and activity-based features, either alone, or in tandem. |
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Springer–Verlag |
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1783-7677 |
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Admin @ si @ DHK2011 |
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1842 |
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