aidotse/LeakPro
Feb 2025 – Apr 2025
3 Months active
Languages Used
JSONJupyter NotebookMarkdownPythonShellTOMLYAMLGit Ignore
Technical Skills
Adversarial AttacksBackend DevelopmentBug FixingCUDAClass DesignClass Inheritance
oscarwernqvist
PROFILE
Oscarwernqvist
Oscar Wernqvist developed core enhancements for the aidotse/LeakPro repository, focusing on generative adversarial network (GAN) model inversion and robust data pipelines. He integrated ResNet18 and expanded model options, aligning training workflows with the PLG-Mi paper and improving experiment reproducibility. Using Python and PyTorch, Oscar refactored configuration management to minimize misconfigurations, introduced dynamic dataset-driven parameters, and implemented FID metric evaluation. He strengthened code quality through linting, notebook hygiene, and dependency management, while adding checkpointing and experiment restart capabilities. His work enabled scalable experimentation, improved evaluation accuracy, and ensured maintainable, clean code for ongoing research in adversarial attacks and privacy.
Overall Statistics
Feature vs Bugs
45%Features
Repository Contributions
104Total
Bugs
32
Commits
104
Features
26
Lines of code
19,208
Activity Months3
Your Network
11 people
Work History
April 2025
35 Commits • 12 Features
Apr 1, 2025April 2025 summary for aidotse/LeakPro: Delivered core model and data-pipeline enhancements aligned with the PLG-Mi paper, expanded experimentation avenues, improved evaluation, and strengthened reproducibility and maintenance. The work focused on delivering business-relevant features, stabilizing training, and enabling scalable experimentation across datasets and models.
35 Commits • 12 Features
Apr 1, 2025April 2025 summary for aidotse/LeakPro: Delivered core model and data-pipeline enhancements aligned with the PLG-Mi paper, expanded experimentation avenues, improved evaluation, and strengthened reproducibility and maintenance. The work focused on delivering business-relevant features, stabilizing training, and enabling scalable experimentation across datasets and models.
April 2025
March 2025
6 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for aidotse/LeakPro. Focused on robustness of data handling for CelebA, dynamic configuration to minimize misconfigurations, and internal API/packaging improvements to enhance developer experience and maintainability. Deliveries emphasize business value through more reliable training runs and easier scalability of experiments.
March 2025
6 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for aidotse/LeakPro. Focused on robustness of data handling for CelebA, dynamic configuration to minimize misconfigurations, and internal API/packaging improvements to enhance developer experience and maintainability. Deliveries emphasize business value through more reliable training runs and easier scalability of experiments.
February 2025
63 Commits • 13 Features
Feb 1, 2025February 2025 for aidotse/LeakPro focused on delivering a robust GAN-based model inversion (MINV) framework, expanding inversion capabilities, and integrating PLGMI-driven defenses, while tightening training observability and CI coverage. Delivered key features including GANHandler with latent_dim (dim_z) support, separation of gan_train.py, latent-space optimization for PLGMI, abstract GAN generator handler, and CelebA PLGMI handler, along with training loop logging enhancements and dataset noise augmentation. Also advanced GAN/configuration and image metrics support (including KNN distance) and expanded test infrastructure (VM-based tests).
63 Commits • 13 Features
Feb 1, 2025February 2025 for aidotse/LeakPro focused on delivering a robust GAN-based model inversion (MINV) framework, expanding inversion capabilities, and integrating PLGMI-driven defenses, while tightening training observability and CI coverage. Delivered key features including GANHandler with latent_dim (dim_z) support, separation of gan_train.py, latent-space optimization for PLGMI, abstract GAN generator handler, and CelebA PLGMI handler, along with training loop logging enhancements and dataset noise augmentation. Also advanced GAN/configuration and image metrics support (including KNN distance) and expanded test infrastructure (VM-based tests).
February 2025
Activity
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Quality Metrics
Correctness84.6%
Maintainability85.4%
Architecture80.8%
Performance74.0%
AI Usage21.4%
Skills & Technologies
Programming Languages
GitGit IgnoreJSONJupyter NotebookMarkdownPythonShellTOMLYAML
Technical Skills
Adversarial AttacksBackend DevelopmentBug FixingCUDAClass DesignClass InheritanceClean CodeCode CleanupCode MaintenanceCode RefactoringCode ReviewCode StructureComputer VisionConfiguration ManagementData Analysis
Repositories Contributed To
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