Robust Task Clustering for Deep and DiverseMulti-Task and Few-Shot Learning
(a) MTL setting (i.e. training on all 69 tasks).
['[BOLD] Model', '[BOLD] Avg Acc']
Since most of the tasks have a significant amount of training data, the single-task baselines achieve good results. Because the conflicts among some tasks (e.g. the 2-star bucket tasks and 5-star bucket tasks require opposite labels on 4-star examples), the holistic MTL-CNN does not show accuracy improvements compared to the single-task methods. It also lags behind the holistic MTL-CNN model trained only on 12 target domains, which indicates that the holistic MTL-CNN cannot leverage large number of background tasks. Our RobustTC-MTL method based on task clustering achieves a significant improvement over all the baselines. Adaptation to New Tasks ( FSL Setting) Due to the limited training resources, all the baselines perform poorly. Our RobustTC-FSL gives far better results compared to all baselines (>6%). It is also significantly better than applying Eq. without clustering (78.85%), i.e. using single-task model from each task instead of cluster-models for P(y|x;⋅). Such result further emphasizes the importance of task clustering for deep models, when better performance could be achieved with such models.