TITLE: Synbols: Probing Learning Algorithms with Synthetic Datasets 

ABSTRACT: Progress in the field of machine learning has been fueled by the introduction
of benchmark datasets pushing the limits of existing algorithms. Enabling the
design of datasets to test specific properties and failure modes of learning
algorithms is thus a problem of high interest, as it has a direct impact on
innovation in the field. In this sense, we introduce Synbols -- Synthetic
Symbols -- a tool for rapidly generating new datasets with a rich composition
of latent features rendered in low resolution images. Synbols leverages the
large amount of symbols available in the Unicode standard and the wide range of
artistic font provided by the open font community. Our tool's high-level
interface provides a language for rapidly generating new distributions on the
latent features, including various types of textures and occlusions. To
showcase the versatility of Synbols, we use it to dissect the limitations and
flaws in standard learning algorithms in various learning setups including
supervised learning, active learning, out of distribution generalization,
unsupervised representation learning, and object counting.
Progress in the field of machine learning has been fueled by the introduction
of benchmark datasets pushing the limits of existing algorithms. Enabling the
design of datasets to test specific properties and failure modes of learning
algorithms is thus a problem of high interest, as it has a direct impact on
innovation in the field. In this sense, we introduce Synbols -- Synthetic
Symbols -- a tool for rapidly generating new datasets with a rich composition
of latent features rendered in low resolution images. Synbols leverages the
large amount of symbols available in the Unicode standard and the wide range of
artistic font provided by the open font community. Our tool's high-level
interface provides a language for rapidly generating new distributions on the
latent features, including various types of textures and occlusions. To
showcase the versatility of Synbols, we use it to dissect the limitations and
flaws in standard learning algorithms in various learning setups including
supervised learning, active learning, out of distribution generalization,
unsupervised representation learning, and object counting.
Synbols: Probing Learning Algorithms with Synthetic Datasets
http://arxiv.org/abs/2009.06415v2