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.
@article{arxiv.1807.03711,
title = {Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful},
author = {Rajesh Chidambaram and Michael Kampffmeyer and Willie Neiswanger and Xiaodan Liang and Thomas Lachmann and Eric Xing},
journal= {arXiv preprint arXiv:1807.03711},
year = {2018}
}