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
Developed core enhancements for the aidotse/LeakPro repository, focusing on adversarial attacks and model inversion using GANs and deep learning techniques. Over three months, delivered 26 features and resolved 32 bugs, improving data handling, experiment reproducibility, and evaluation workflows. Integrated ResNet18 and expanded model options, implemented dynamic configuration for CelebA, and introduced FID and KNN metrics for robust evaluation. Leveraged Python, PyTorch, and YAML to refactor code structure, streamline experiment execution, and strengthen checkpoint management. Emphasized clean code, maintainability, and scalable experimentation, while enhancing documentation, dependency management, and developer experience through improved packaging and internal API clarity.
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
14 peopleShared Repositories
14
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
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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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