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LinSlot: Exploiting Linear Representation hypothesis for unsupervised attribute discovery from slot based object representation
This paper studies the problem of learning disentangled representations of objects and their attributes from raw, unstructured image data. Slot-based methods have shown considerable success in unsupervised learning of object representations from images. Block-slot attention-based methods extend this
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