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• W3080147302 abstract "S ummary Apart from discriminative models for classification and object detection tasks, the application of deep convolutional neural networks to basic research utilizing natural imaging data has been somewhat limited; particularly in cases where a set of interpretable features for downstream analysis is needed, a key requirement for many scientific investigations. We present an algorithm and training paradigm designed specifically to address this: decontextualized hierarchical representation learning (DHRL). By combining a generative model chaining procedure with a ladder network architecture and latent space regularization for inference, DHRL address the limitations of small datasets and encourages a disentangled set of hierarchically organized features. In addition to providing a tractable path for analyzing complex hierarchal patterns using variation inference, this approach is generative and can be directly combined with empirical and theoretical approaches. To highlight the extensibility and usefulness of DHRL, we demonstrate this method in application to a question from evolutionary biology." @default.
• W3080147302 created "2020-09-01" @default.
• W3080147302 creator A5031215616 @default.
• W3080147302 creator A5033076875 @default.
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• W3080147302 date "2020-08-25" @default.
• W3080147302 modified "2023-09-24" @default.
• W3080147302 title "Decontextualized learning for interpretable hierarchical representations of visual patterns" @default.
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• W3080147302 doi "https://doi.org/10.1101/2020.08.25.266593" @default.
• W3080147302 hasPublicationYear "2020" @default.
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