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Latent space analysis and generalization to out-of-distribution data

Katie Rainey, Erin Hausmann, Donald Waagen, David Gray, Donald Hulsey

TL;DR

The paper investigates whether latent-space out-of-distribution (OOD) signals align with classifier accuracy when deploying SAR imagery classifiers. It analyzes paired synthetic-real SAR datasets (SAMPLE and MSTAR) using a ResNet-20 classifier with a latent space of dimension $256$ and a deep $k$-nearest neighbor OOD detector to assess in-task versus out-of-task data. The key finding is that latent-space OOD status is not a reliable proxy for classification performance across datasets and model instances, highlighting gaps between OOD signals and real-world robustness. The work emphasizes the need to understand latent-space geometry to improve robustness and generalization under domain shifts for SAR analysis.

Abstract

Understanding the relationships between data points in the latent decision space derived by the deep learning system is critical to evaluating and interpreting the performance of the system on real world data. Detecting \textit{out-of-distribution} (OOD) data for deep learning systems continues to be an active research topic. We investigate the connection between latent space OOD detection and classification accuracy of the model. Using open source simulated and measured Synthetic Aperture RADAR (SAR) datasets, we empirically demonstrate that the OOD detection cannot be used as a proxy measure for model performance. We hope to inspire additional research into the geometric properties of the latent space that may yield future insights into deep learning robustness and generalizability.

Latent space analysis and generalization to out-of-distribution data

TL;DR

The paper investigates whether latent-space out-of-distribution (OOD) signals align with classifier accuracy when deploying SAR imagery classifiers. It analyzes paired synthetic-real SAR datasets (SAMPLE and MSTAR) using a ResNet-20 classifier with a latent space of dimension and a deep -nearest neighbor OOD detector to assess in-task versus out-of-task data. The key finding is that latent-space OOD status is not a reliable proxy for classification performance across datasets and model instances, highlighting gaps between OOD signals and real-world robustness. The work emphasizes the need to understand latent-space geometry to improve robustness and generalization under domain shifts for SAR analysis.

Abstract

Understanding the relationships between data points in the latent decision space derived by the deep learning system is critical to evaluating and interpreting the performance of the system on real world data. Detecting \textit{out-of-distribution} (OOD) data for deep learning systems continues to be an active research topic. We investigate the connection between latent space OOD detection and classification accuracy of the model. Using open source simulated and measured Synthetic Aperture RADAR (SAR) datasets, we empirically demonstrate that the OOD detection cannot be used as a proxy measure for model performance. We hope to inspire additional research into the geometric properties of the latent space that may yield future insights into deep learning robustness and generalizability.

Paper Structure

This paper contains 15 sections, 1 equation, 4 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Experimental Design
  3. Datasets
  4. Models and Training
  5. Deep K-Nearest Neighbors for Outlier Detection
  6. Outlier Analysis and Classification Results
  7. Outlier Analysis
  8. Classification Results
  9. Discussion
  10. Conclusion
  11. Acknowledgements
  12. Deep $k$-Nearest Neighbor Algorithm
  13. Experiment Details
  14. Representative Model Selection
  15. P-Value Analysis

Figures (4)

  • Figure 1: Overview of Unaugmented Datasets
  • Figure 2: Examples of additive Gaussian noise augmentation of SAMPLE simulated images with augmented images with $\sigma_{noise} = 1.2$
  • Figure 3: MSTAR validation set accuracies for the ResNet-20 models trained with additive Gaussian noise augmentation ($\sigma_{noise} = 1.2$)
  • Figure 4: Scatter plots of model instance mean class accuracies versus deep kNN identified inliers for the 4 SAR datasets, across all 50 model instances ($k = \{1, 10, 100\}$ and $\alpha = 0.1$)