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ServiceNow completed its acquisition of Element AI on January 8, 2021. All references to Element AI in the materials that are part of this project should refer to ServiceNow.


Probing Learning Algorithms with Synthetic Datasets

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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.



This is the code repository for the Synbols dataset generator. Dataloaders and examples such as image classification can be found in


The easiest way to install Synbols is via PyPI. Simply run the following command:

pip install synbols

Software dependencies

Synbols relies on fonts and system packages. To ensure reproducibility, we provide a Docker image with everything preinstalled. Thus, the only dependency is Docker (see here to install).


Using predefined generators

$ synbols-datasets --help
$ synbols-datasets --dataset=some-large-occlusion --n_samples=1000 --seed=42

Generating some-large-occlusion dataset. Info: With probability 20%, add a large occlusion over the existing symbol.
Preview generated.
 35%|############################2                                                   | 353/1000 [00:05<00:10, 63.38it/s]

Defining your own generator

Examples of how to create new datasets can be found in the examples directory.

def translation(rng):
    """Generates translations uniformly from (-2, 2), going outside of the box."""
    return tuple(rng.uniform(low=-2, high=2, size=2))

# Modifies the default attribute sampler to fix the scale to a constant and the (x,y) translation to a new distribution
attr_sampler = basic_attribute_sampler(scale=0.5, translation=translation)

generate_and_write_dataset(dataset_path, attr_sampler, n_samples)

To generate your dataset, you need to run your code in the Synbols runtime environment. This is done using the synbols command as follows:

synbols --foo bar

Launch the example notebook

We provide an example Jupyter notebook in the examples directory. To run this notebook, first download it locally and run the following command at the notebook's location:


This will launch jupyter notebook in the Synbols runtime environment and allow you to access it via your browser.


For any bug or feature requests, please create an issue.