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Fine-Grained Change Detection and Regression Analysis are essential in manyapplications of ArtificialIntelligence. In practice, this task is oftenchallenging owing to the lack of reliable ground truth informationandcomplexity arising from interactions between the many underlying factorsaffecting a system. Therefore,developing a framework which can represent therelatedness and reliability of multiple sources of informationbecomes critical.In this paper, we investigate how techniques in multi-task metric learning canbe applied for theregression of fine-grained change in real data.The key ideais that if we incorporate the incremental change in a metric of interestbetween specific instancesof an individual object as one of the tasks in amulti-task metric learning framework, then interpreting thatdimension willallow the user to be alerted to fine-grained change invariant to what theoverall metric isgeneralised to be. The techniques investigated arespecifically tailored for handling heterogeneous data sources,i.e. the inputdata for each of the tasks might contain missing values, the scale andresolution of the values is notconsistent across tasks and the data containsnon-independent and identically distributed (non-IID) instances. Wepresent theresults of our initial experimental implementations of this idea and discussrelated research in thisdomain which may offer direction for further research.

Regressing Relative Fine-Grained Change for Sub-Groups in Unreliable Heterogeneous Data Through Deep Multi-Task Metric Learning