Contributed to the MHC-FA24-CS341CV/beyond-the-pixels-emerging-computer-vision-research-topics-fa24 repository by developing end-to-end Conditional GAN (CGAN) and Super-Resolution GAN (SRGAN) training pipelines using TensorFlow and Keras. Focused on enhancing reproducibility and maintainability, the work included a benchmarking utility for comparing training times and detailed documentation to support onboarding and future research. Addressed shape compatibility issues during SRGAN training and improved repository hygiene by removing obsolete artifacts and consolidating related content. Leveraged Python and Jupyter Notebook to streamline experimental workflows, resulting in a cleaner codebase and more robust infrastructure for ongoing computer vision and deep learning experiments.
November 2024 (2024-11) monthly summary for MHC-FA24-CS341CV/beyond-the-pixels-emerging-computer-vision-research-topics-fa24. Delivered end-to-end GAN experimentation and documentation improvements with a focus on reproducibility, maintainability, and onboarding efficiency. Key features delivered include CGAN and SRGAN training pipelines in TensorFlow/Keras with a benchmarking utility; formal GAN documentation under doc/07-gan; and repository cleanup to remove obsolete artifacts.
November 2024 (2024-11) monthly summary for MHC-FA24-CS341CV/beyond-the-pixels-emerging-computer-vision-research-topics-fa24. Delivered end-to-end GAN experimentation and documentation improvements with a focus on reproducibility, maintainability, and onboarding efficiency. Key features delivered include CGAN and SRGAN training pipelines in TensorFlow/Keras with a benchmarking utility; formal GAN documentation under doc/07-gan; and repository cleanup to remove obsolete artifacts.
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