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While the promises of Multi-Task Learning (MTL) are attractive,characterizing the conditions of its success is still an open problem in DeepLearning. Some tasks may benefit from being learned together while others maybe detrimental to one another. From a task perspective, grouping cooperativetasks while separating competing tasks is paramount to reap the benefits ofMTL, i.e., reducing training and inference costs. Therefore, estimating taskaffinity for joint learning is a key endeavor. Recent work suggests that thetraining conditions themselves have a significant impact on the outcomes ofMTL. Yet, the literature is lacking of a benchmark to assess the effectivenessof tasks affinity estimation techniques and their relation with actual MTLperformance. In this paper, we take a first step in recovering this gap by (i)defining a set of affinity scores by both revisiting contributions fromprevious literature as well presenting new ones and (ii) benchmarking them onthe Taskonomy dataset. Our empirical campaign reveals how, even in asmall-scale scenario, task affinity scoring does not correlate well with actualMTL performance. Yet, some metrics can be more indicative than others.

"It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning