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- W4285135890 abstract "AbstractCompositions of concepts from human-annotated datasets, e.g., “chair OR table”, have been shown to approximate latent representations of image classifiers better than single concepts. In this work, we introduce the Close Algorithm, which improves performance according to the IoU metric by utilizing the non-logical connectors CLOSE-TO, WITH, and EXPAND. We consider the shortcomings of current approaches, discuss possible causes, and review a small user study we have run to collect evidence on this point. We also introduce a metric that discourages the reliance on scene-level annotations. (The code to replicate the technical results (along with additional sample images) can be accessed at https://github.com/rafaelharth/indres). KeywordsMachine learningImage classificationInterpretability" @default.
- W4285135890 created "2022-07-14" @default.
- W4285135890 creator A5050452379 @default.
- W4285135890 date "2022-01-01" @default.
- W4285135890 modified "2023-09-27" @default.
- W4285135890 title "Understanding Individual Neurons of ResNet Through Improved Compositional Formulas" @default.
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- W4285135890 doi "https://doi.org/10.1007/978-3-031-09282-4_24" @default.
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