KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligence

Holzinger, Andreas; Saranti, Anna; Mueller, Heimo

doi:10.48550/arxiv.2103.00519

Cited by 2 publications

(2 citation statements)

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“…The Compositional Reasoning Under Uncertainty (CURI) benchmark uses the CLEVR renderer to construct a test bed for compositional and relational learning under uncertainty [49]. [22] provide an extensive survey of further experimental diagnostic benchmarks for analyzing explainable machine learning frameworks along with proposing the KandinskyPATTERNS benchmark that contains synthetic images with simple 2-dimensional objects. It can be used for testing the quality of explanations and concept learning.…”

Section: Related Workmentioning

confidence: 99%

CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations

Salewski,

Koepke,

Lensch

et al. 2022

Preprint

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Providing explanations in the context of Visual Question Answering (VQA) presents a fundamental problem in machine learning. To obtain detailed insights into the process of generating natural language explanations for VQA, we introduce the large-scale CLEVR-X dataset that extends the CLEVR dataset with natural language explanations. For each image-question pair in the CLEVR dataset, CLEVR-X contains multiple structured textual explanations which are derived from the original scene graphs. By construction, the CLEVR-X explanations are correct and describe the reasoning and visual information that is necessary to answer a given question. We conducted a user study to confirm that the groundtruth explanations in our proposed dataset are indeed complete and relevant. We present baseline results for generating natural language explanations in the context of VQA using two state-of-the-art frameworks on the CLEVR-X dataset. Furthermore, we provide a detailed analysis of the explanation generation quality for different question and answer types. Additionally, we study the influence of using different numbers of ground-truth explanations on the convergence of natural language generation (NLG) metrics. The CLEVR-X dataset is publicly available at https://explainableml.github.io/CLEVR-X/.

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Section: Related Workmentioning

confidence: 99%

CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations

Salewski,

Koepke,

Lensch

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Additionally, researchers suggest keeping humans in the loop-considering expert knowledge in interpreting the ML/DL results-leads to user trust and identifying points of model failure (Holzinger, 2016;Magister et al, 2021). In recognition of the importance of transparency in models defined for the medical imaging data, dedicated datasets and XAI exploration environments were recently proposed (Holzinger et al, 2021). Due to the nascent nature of the neuroimaging filed and its extensive use in deep learning studies, techniques such as magnetic resonance imaging (MRI), functional MRI (fMRI), computerized tomography (CT), and ultrasound, have considerably piqued the interest of XAI researchers (Zhu et al, 2019;van der Velden et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Explainable AI: A review of applications to neuroimaging data

et al. 2022

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Deep neural networks (DNNs) have transformed the field of computer vision and currently constitute some of the best models for representations learned via hierarchical processing in the human brain. In medical imaging, these models have shown human-level performance and even higher in the early diagnosis of a wide range of diseases. However, the goal is often not only to accurately predict group membership or diagnose but also to provide explanations that support the model decision in a context that a human can readily interpret. The limited transparency has hindered the adoption of DNN algorithms across many domains. Numerous explainable artificial intelligence (XAI) techniques have been developed to peer inside the “black box” and make sense of DNN models, taking somewhat divergent approaches. Here, we suggest that these methods may be considered in light of the interpretation goal, including functional or mechanistic interpretations, developing archetypal class instances, or assessing the relevance of certain features or mappings on a trained model in a post-hoc capacity. We then focus on reviewing recent applications of post-hoc relevance techniques as applied to neuroimaging data. Moreover, this article suggests a method for comparing the reliability of XAI methods, especially in deep neural networks, along with their advantages and pitfalls.

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KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligence

Cited by 2 publications

References 0 publications

CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations

CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations

Explainable AI: A review of applications to neuroimaging data

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