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We propose JEDI, a multi-dataset semi-supervised learning method, whichefficiently combines knowledge from multiple experts, learned on differentdatasets, to train and improve the performance of individual, per dataset,student models. Our approach achieves this by addressing two important problemsin current machine learning research: generalization across datasets andlimitations of supervised training due to scarcity of labeled data. We startwith an arbitrary number of experts, pretrained on their own specific dataset,which form the initial set of student models. The teachers are immediatelyderived by concatenating the feature representations from the penultimatelayers of the students. We then train all models in a student-teachersemi-supervised learning scenario until convergence. In our efficient approach,student-teacher training is carried out jointly and end-to-end, showing thatboth students and teachers improve their generalization capacity duringtraining. We validate our approach on four video action recognition datasets.By simultaneously considering all datasets within a unified semi-supervisedsetting, we demonstrate significant improvements over the initial experts.

JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition