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Training deep networks requires various design decisions regarding forinstance their architecture, data augmentation, or optimization. In this work,we find these training variations to result in networks learning unique featuresets from the data. Using public model libraries comprising thousands of modelstrained on canonical datasets like ImageNet, we observe that for arbitrarypairings of pretrained models, one model extracts significant data contextunavailable in the other -- independent of overall performance. Given anyarbitrary pairing of pretrained models and no external rankings (such asseparate test sets, e.g. due to data privacy), we investigate if it is possibleto transfer such "complementary" knowledge from one model to another withoutperformance degradation -- a task made particularly difficult as additionalknowledge can be contained in stronger, equiperformant or weaker models. Yetfacilitating robust transfer in scenarios agnostic to pretrained model pairingswould unlock auxiliary gains and knowledge fusion from any model repositorywithout restrictions on model and problem specifics - including from weaker,lower-performance models. This work therefore provides an initial, in-depthexploration on the viability of such general-purpose knowledge transfer. Acrosslarge-scale experiments, we first reveal the shortcomings of standard knowledgedistillation techniques, and then propose a much more general extension throughdata partitioning for successful transfer between nearly all pretrained models,which we show can also be done unsupervised. Finally, we assess both thescalability and impact of fundamental model properties on successfulmodel-agnostic knowledge transfer.

Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model