We are seeking a skilled professional to assist in replicating a scientific experiment and identifying ways to enhance its outcomes. The original paper has a clear limitation that we need to address to introduce a contribution. The ideal candidate should have a strong background in computer vision. Your role will involve understanding the original experiment, replicate and provide running code on the experiment, and implement modifications to improve results and fill the research limitation. If you have a passion for research and a proven track record in experimental methods, we would love to hear from you! Scope • Replication: mirror the author’s data preprocessing, hyper-parameters, and augmentation schedule until our accuracy matches their reported numbers (±1 %). • Explainability upgrade: introduce negative concepts explanation. • Packaging: clean, modular Python code plus a short README or notebook that walks through reproduction, visualisation, and how to plug in a new dataset. Acceptance criteria 1. Baseline reproduced on my GPU with matching metrics. 2. At least one quantitative faithfulness measure (e.g., deletion/insertion AUC) showing improvement over the baseline. 3. Visual explanation examples exported for a held-out batch in both PDF and PNG. When you apply, point me to past work where you rebuilt or tuned training scripts and added XAI tooling; links to GitHub repos, papers, or demos are perfect. I am most interested in your concrete results rather than a long proposal—evidence speaks louder here.