Abstract
One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented them accordingly. Finally, with the lessons learned in the previous step, we propose a set of mitigation strategies to overcome the main reproducibility challenges and help researchers achieve their goals.