Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Abstract: Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations. To address this issue we introduce Morpho-MNIST, a framework that aims to answer: "to what extent has my model learned to represent specific factors of variation in the data?" We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of trained models, identification of the roles of latent variables, and characterisation of sample diversity. We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation. Data and code are available at https://github.com/dccastro/Morpho-MNIST.

• The paper introduces a novel quantitative framework that extends MNIST with morphometric metrics for objective evaluation of learning models.
• It incorporates systematic perturbations like local swelling and fractures to challenge both supervised and unsupervised tasks.
• The approach offers diagnostic tools to assess disentanglement, mode collapse, and robustness, advancing model interpretability.

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

The paper "Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning" provides a novel framework that leverages the iconic MNIST dataset to facilitate a more rigorous evaluation of representation learning models. The authors propose Morpho-MNIST as a means to quantitatively assess the capability of machine learning models in capturing specific morphological variations within data, thereby addressing a critical shortfall in the current landscape— the absence of standardized benchmarks for disentanglement and representation learning.

Core Contributions

Morpho-MNIST extends the original MNIST dataset with additional morphological features, facilitating an objective evaluation of representation learning models. The key contributions include:

1. Morphometrics: The paper proposes morphometric measurements such as stroke thickness, length, width, height, and slant as quantifiable descriptors of digit shapes. These metrics enable a standardized form of comparison across diverse models and experiments.
2. Perturbations: The authors introduce systematic perturbations to the dataset, including local perturbations (swelling and fractures) and global changes (thinning and thickening), creating novel challenges and benchmarking opportunities for both supervised and unsupervised learning tasks such as outlier detection and domain adaptation.
3. Quantitative Evaluation Protocols: By providing tools for morphometric analysis and facilitating the measurement of generated samples, the paper enables quantitative evaluations that were previously solely reliant on subjective visual inspection. This framework assists in the diagnosis of common generative modeling issues like mode collapse.

Implications and Experimental Insights

The Morpho-MNIST framework can potentially reshape the evaluation strategies for generative and representation learning models:

• Enhanced Insight into Disentanglement: Through morphometric analysis, it is possible to quantitatively assess whether latent variables in models like variational autoencoders (VAEs) and generative adversarial networks (GANs) are capturing distinct factors of variation in the input data. This work provides methodologies for both inferential and generative disentanglement evaluations.
• Diagnostic of Generative Models: The proposed framework allows researchers to characterize the diversity and faithfulness of samples generated by models. Kernel two-sample tests based on morphometrics, for instance, give a structured method to evaluate distributional similarities and diagnose mode collapse scenarios.
• Robustness Testing: By mimicking domain shift through controlled perturbations, researchers can assess model robustness and performance under various scenarios, facilitating development toward more generalizable machine learning models.

The authors demonstrate practical applications and adaptability of their framework through case studies involving different model architectures and datasets, including extensions beyond MNIST. These studies illustrate the potential for Morpho-MNIST to contribute significantly to research efforts in both academic and applied machine learning contexts.

Conclusion and Future Prospects

By extending MNIST with quantifiable shape descriptors and perturbations, Morpho-MNIST provides an invaluable toolset for the detailed assessment of representation learning models. Its quantitative approach to evaluating generative and predictive modeling techniques can yield deeper insights into model interpretability and performance. Furthermore, the presented methodologies can serve as a platform that can be applied to other rasterized datasets, broadening the scope for robust and comprehensive model analysis in machine learning research. This initiates a significant step towards creating more expressive metrics for understanding and improving representation learning frameworks.