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Diversity vs. Recognizability: Human-like generalization in one-shot generative models
Victor Boutin · Lakshya Singhal · Xavier Thomas · Thomas Serre

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #124

Robust generalization to new concepts has long remained a distinctive feature of human intelligence. However, recent progress in deep generative models has now led to neural architectures capable of synthesizing novel instances of unknown visual concepts from a single training example. Yet, a more precise comparison between these models and humans is not possible because existing performance metrics for generative models (i.e., FID, IS, likelihood) are not appropriate for the one-shot generation scenario. Here, we propose a new framework to evaluate one-shot generative models along two axes: sample recognizability vs. diversity (i.e., intra-class variability). Using this framework, we perform a systematic evaluation of representative one-shot generative models on the Omniglot handwritten dataset. We first show that GAN-like and VAE-like models fall on opposite ends of the diversity-recognizability space. Extensive analyses of the effect of key model parameters further revealed that spatial attention and context integration have a linear contribution to the diversity-recognizability trade-off. In contrast, disentanglement transports the model along a parabolic curve that could be used to maximize recognizability. Using the diversity-recognizability framework, we were able to identify models and parameters that closely approximate human data.

Author Information

Victor Boutin (Brown university)
Lakshya Singhal (Indian Institute of Technology Delhi)
Xavier Thomas (Manipal Institute of Technology)
Thomas Serre (Brown University)

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