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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces

Daniel Geissler, Bo Zhou, Mengxi Liu, Paul Lukowicz

TL;DR

Latent Boost tackles the opacity of supervised classifiers by embedding a distance-metric–based objective into the latent space, promoting tightly clustered, interpretable class representations without sacrificing accuracy. The approach combines a weighted loss that fuses a distance-metric term with cross-entropy, selects Magnet loss as a backbone, and evolves it with PCA-based dimensionality reduction, per-cluster variance, and dynamic alpha/beta schedules to balance intra- and inter-cluster structure. Empirical results across Fashion MNIST, CIFAR-10, and CIFAR-100 show consistent accuracy and Micro-F1 gains, faster convergence, and improved latent-space interpretability as measured by Silhouette scores, with notable improvements on smaller to mid-complexity datasets. The work highlights practical benefits for interpretability and training efficiency while signaling avenues for refinement in dynamic weighting, clustering assumptions, and out-of-distribution robustness.

Abstract

Supervised machine learning often operates on the data-driven paradigm, wherein internal model parameters are autonomously optimized to converge predicted outputs with the ground truth, devoid of explicitly programming rules or a priori assumptions. Although data-driven methods have yielded notable successes across various benchmark datasets, they inherently treat models as opaque entities, thereby limiting their interpretability and yielding a lack of explanatory insights into their decision-making processes. In this work, we introduce Latent Boost, a novel approach that integrates advanced distance metric learning into supervised classification tasks, enhancing both interpretability and training efficiency. Thus during training, the model is not only optimized for classification metrics of the discrete data points but also adheres to the rule that the collective representation zones of each class should be sharply clustered. By leveraging the rich structural insights of intermediate model layer latent representations, Latent Boost improves classification interpretability, as demonstrated by higher Silhouette scores, while accelerating training convergence. These performance and latent structural benefits are achieved with minimum additional cost, making it broadly applicable across various datasets without requiring data-specific adjustments. Furthermore, Latent Boost introduces a new paradigm for aligning classification performance with improved model transparency to address the challenges of black-box models.

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces

TL;DR

Latent Boost tackles the opacity of supervised classifiers by embedding a distance-metric–based objective into the latent space, promoting tightly clustered, interpretable class representations without sacrificing accuracy. The approach combines a weighted loss that fuses a distance-metric term with cross-entropy, selects Magnet loss as a backbone, and evolves it with PCA-based dimensionality reduction, per-cluster variance, and dynamic alpha/beta schedules to balance intra- and inter-cluster structure. Empirical results across Fashion MNIST, CIFAR-10, and CIFAR-100 show consistent accuracy and Micro-F1 gains, faster convergence, and improved latent-space interpretability as measured by Silhouette scores, with notable improvements on smaller to mid-complexity datasets. The work highlights practical benefits for interpretability and training efficiency while signaling avenues for refinement in dynamic weighting, clustering assumptions, and out-of-distribution robustness.

Abstract

Supervised machine learning often operates on the data-driven paradigm, wherein internal model parameters are autonomously optimized to converge predicted outputs with the ground truth, devoid of explicitly programming rules or a priori assumptions. Although data-driven methods have yielded notable successes across various benchmark datasets, they inherently treat models as opaque entities, thereby limiting their interpretability and yielding a lack of explanatory insights into their decision-making processes. In this work, we introduce Latent Boost, a novel approach that integrates advanced distance metric learning into supervised classification tasks, enhancing both interpretability and training efficiency. Thus during training, the model is not only optimized for classification metrics of the discrete data points but also adheres to the rule that the collective representation zones of each class should be sharply clustered. By leveraging the rich structural insights of intermediate model layer latent representations, Latent Boost improves classification interpretability, as demonstrated by higher Silhouette scores, while accelerating training convergence. These performance and latent structural benefits are achieved with minimum additional cost, making it broadly applicable across various datasets without requiring data-specific adjustments. Furthermore, Latent Boost introduces a new paradigm for aligning classification performance with improved model transparency to address the challenges of black-box models.

Paper Structure

This paper contains 20 sections, 12 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Distance Metric Learning Theoretical Background
  5. Incorporating Distance Metrics in Multi-Class Classification
  6. Experiment Setup
  7. Superiority of Magnet Loss
  8. Latent Boost Evolution
  9. Condensed Distance Information
  10. Individual Cluster Variance
  11. Dynamic Inter and Intra Cluster Balance
  12. Full Potential of Latent Boost
  13. Improved Classification Performance
  14. Improved Latent Representation Interpretability
  15. Discussion
  16. ...and 5 more sections

Figures (3)

  • Figure 1: Oppose to traditional training, relying on probabilistic loss only, Latent Boost injects distance metric information, obtained from the model's hidden latent representations, as addition into the training through balanced weighted sum equations.
  • Figure 2: Flowchart of the Latent Boost approach, summarizing the mathematical steps to embed distance-metric information into the classic probabilistic training.
  • Figure 3: Comparison of baseline ($\lambda$=0), standard Magnet loss, and our Latent Boost approach across the three experiment setups.