Caleb Parks
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Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Caleb Parks' research focuses on the application of advanced neural network architectures and machine learning techniques, particularly for synthetic aperture radar (SAR) automatic target recognition (ATR). His work investigates methods for bridging the gap between synthetic and measured data in SAR imaging, exploring techniques such as style and content splitting, and enforcing feature correlation on generative adversarial networks (GANs). Parks has also examined the value of phase information in deep SAR ATR and developed novel attention mechanisms for neural networks, including OrthoNets and WaveNets.
His scholarly output includes six publications, with a notable recent contribution in 2025. Parks has collaborated with Susan Gauch, Ryan Socha, and John M. Gauch at the University of Arkansas at Fayetteville, with whom he shares multiple publications. His research is characterized by its focus on developing and evaluating sophisticated computational models for complex pattern recognition tasks.
Metrics
- h-index: 2
- Publications: 6
- Citations: 50
Selected Publications
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Assessing the value of phase to deep SAR ATR via noninferiority testing (2025)
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Graph pretraining approach to utilize synthetic data for SAR ATR (2024)
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OrthoNets: Orthogonal Channel Attention Networks (2023)
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Bridging the synthetic to measured SAR gap by splitting style and content (2023)
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WaveNets: Wavelet Channel Attention Networks (2022)
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Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images (2022)
Collaboration Network
Top Collaborators
- OrthoNets: Orthogonal Channel Attention Networks
- Bridging the synthetic to measured SAR gap by splitting style and content
- Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images
- Bridging the synthetic to measured SAR gap by splitting style and content
- Graph pretraining approach to utilize synthetic data for SAR ATR
- Assessing the value of phase to deep SAR ATR via noninferiority testing
- OrthoNets: Orthogonal Channel Attention Networks
- WaveNets: Wavelet Channel Attention Networks
- WaveNets: Wavelet Channel Attention Networks
- Bridging the synthetic to measured SAR gap by splitting style and content
- Graph pretraining approach to utilize synthetic data for SAR ATR
- Assessing the value of phase to deep SAR ATR via noninferiority testing
- Graph pretraining approach to utilize synthetic data for SAR ATR
- Assessing the value of phase to deep SAR ATR via noninferiority testing
- Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images
- Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images
- Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images
- WaveNets: Wavelet Channel Attention Networks
- OrthoNets: Orthogonal Channel Attention Networks
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