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Learning a face space for experiments on human identity
Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on alignment of the latent representation to human psychological representations and the photorealism of the generated images.
Capturing Human Category Representations by Sampling in Deep Feature Spaces
In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners.
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