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arXiv · 1807.03711

Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful

Rajesh Chidambaram·Michael Kampffmeyer·Willie Neiswanger·Xiaodan Liang·Thomas Lachmann·Eric Xing
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Abstract

Raven's Progressive Matrices are one of the widely used tests in evaluating the human test taker's fluid intelligence. Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models. Our empirical research analysis on state-of-the-art generative models discern their ability to generalize concepts across classes. In the process, we introduce Infinite World, an evaluable, scalable, multi-modal, light-weight dataset and Zero-Shot Intelligence Metric ZSI. The proposed tests condenses human-level spatial and numerical reasoning tasks to its simplistic geometric forms. The dataset is scalable to a theoretical limit of infinity, in numerical features of the generated geometric figures, image size and in quantity. We systematically analyze state-of-the-art model's internal consistency, identify their bottlenecks and propose a pro-active optimization method for few-shot and zero-shot learning.

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Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric Xing. 2018-07-10. Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful. https://arxiv.org/abs/1807.03711

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