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VAE Dataset Normalizer Roadmap

Current Status

Active development phase, with core features implemented and nearing a v1.0.0 stable release.

Milestones

v0.1.0 - Foundation

  • ✓ Core functionality

  • ✓ Basic documentation

  • ✓ CI/CD pipeline

v1.0.0 - Stable Release

  • ✓ RSR (Rhodium Standard Repository) compliance

  • ✓ Full documentation suite (including detailed READMEs, CONTRIBUTING, SECURITY, GOVERNANCE, ACCOUNTABILITY, REVERSIBILITY)

  • ✓ Diff-based compression: Store VAE images as diffs to reduce dataset size by ~50%

  • ✓ compress, decompress, reconstruct subcommands

  • ✓ Julia CompressedVAEDataset for on-the-fly VAE reconstruction

  • ✓ Diff encoding: diff = VAE - Original + 128

  • ✓ Contrastive learning model for VAE artifact detection (contrastive_model.jl)

  • ✓ CNN encoder with ResNet-style residual blocks

  • ✓ Projection head for contrastive learning

  • ✓ Multiple loss functions: NT-Xent, Supervised Contrastive, Triplet, Contrastive

  • ✓ Two-phase training: contrastive pre-training + classifier fine-tuning

  • ✓ Binary classifier for original vs VAE discrimination

  • ✓ Embedding extraction for visualization

  • ✓ Evaluation metrics: accuracy, precision, recall, F1, confusion matrix

  • ✓ GPU support via CUDA.jl (optional)

  • ✓ Justfile recipes for training and evaluation

  • ❏ Comprehensive tests (Rust unit, integration, fuzz tests; Julia unit tests)

  • ❏ Production ready

Future Directions

Future development will focus on expanding the utility and robustness of the VAE Dataset Normalizer, driven by community feedback and emerging research needs. Potential areas include:

  • Expanded VAE Artifact Detection Models: Explore alternative contrastive learning architectures, semi-supervised approaches, and integration with other artifact detection techniques.

  • Multi-Modal Artifact Detection: Extend the framework to detect artifacts in other data modalities (e.g., text, audio) generated by large models.

  • Explainability (XAI) for Artifacts: Develop methods to explain why a particular image is flagged as a VAE artifact, providing insights into the nature of the detected degradation.

  • Integration with broader ML Ecosystem: Streamline integration with popular ML platforms and tools, potentially through additional language bindings or standardized APIs.

  • Performance Optimization: Further optimize data loading, processing, and model inference for even larger datasets and faster turnaround times.

  • Formal Verification for ML Models: Expand the application of formal methods beyond data splits to verify properties of the ML models themselves, such as robustness or fairness.

  • Community Contributions: Actively encourage and integrate community-driven features and improvements.