Industrial Anomaly Detection Model Development

Job ID: 40095834

Budget: $30 – $250 USD

An existing machine learning anomaly detection model needs to be improved to research-publication quality. This is research-level work, not basic coding or copy-paste implementation. The focus is on architectural refinement, novelty, and rigorous experimental design.
The model must be strengthened with clear multiscale feature extraction, a well-defined fusion mechanism, and improved core components including the memory bank and decoder. The final model should introduce defensible novel contributions and clearly address limitations of current SOTA approaches.
Work includes reviewing recent research papers, identifying gaps, redesigning model components, and providing fair comparisons with SOTA models. Direct or simple implementation from existing papers or repositories is not allowed; ideas may be inspired by prior work but must be modified and integrated in a novel way.

Deliverables include improved model code, quantitative results, visualizations (anomaly maps), clear architecture and pipeline diagrams (Draw.io), and a detailed explanation of the model’s working and novelty. Code must be executed only on the provided server via UltraViewer due to data confidentiality.
Budget: USD $200 (model already exists