Efficient hyperparameter optimization for ATR using homotopy parametrization

Sophia Abraham; Jeffrey Kinnison; Zachary Miksis; Domenick Poster; Suya You; Jonathan D. Hauenstein; Walter Scheirer

doi:10.1117/12.2662753

13 June 2023 Efficient hyperparameter optimization for ATR using homotopy parametrization

Sophia Abraham, Jeffrey Kinnison, Zachary Miksis, Domenick Poster, Suya You, Jonathan D. Hauenstein, Walter Scheirer

Proceedings Volume 12521, Automatic Target Recognition XXXIII; 1252107 (2023) https://doi.org/10.1117/12.2662753
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Deep learning has expedited important breakthroughs in research and commercial applications for next-generation technologies across many domains including Automatic Target Recognition (ATR). The success of these models in a specific application is often attributed to optimized hyperparameters: user-configured values controlling the model’s ability to learn from data. Tuning hyperparameters however remains a difficult and computationally expensive task contributing to deficient ATR model performance compared to set requirements. We present the efficacy of applying our developed hyperparameter optimization method to boost the effectiveness and performance of any given optimization method. Specifically, we use a generalized additive model surrogate homotopy hyperparameter optimization strategy to approximate regions of interest and trace minimal points over regions of the hyperparameter space instead of ineffectively evaluating the entire hyperparameter surface. We integrate our approach into SHADHO (Scalable Hardware-Aware Distributed Hyperparameter Optimization) a hyperparameter optimization framework that computes the relative complexity of each search space and then monitors the performance of the learning task over the trials. We demonstrate how our approach effectively finds optimal hyperparameters for object detection by conducting a model search to optimize multiple object detection algorithms on a subset of the DSIAC ATR Algorithm Development Image Database and finding models that achieve comparable or lower validation loss in fewer iterations than standard techniques and manual tuning practices.

Conference Presentation

Citation Download Citation

Sophia Abraham, Jeffrey Kinnison, Zachary Miksis, Domenick Poster, Suya You, Jonathan D. Hauenstein, and Walter Scheirer "Efficient hyperparameter optimization for ATR using homotopy parametrization", Proc. SPIE 12521, Automatic Target Recognition XXXIII, 1252107 (13 June 2023); https://doi.org/10.1117/12.2662753

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