MEAL uses adversarial block-wise distillation from randomly selected teacher networks to train a single student that outperforms both the individual teachers and conventional ensembles at no extra inference cost.
Openimages: A public dataset for large-scale multi-label and multi-class image classification
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Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
MEAL uses adversarial block-wise distillation from randomly selected teacher networks to train a single student that outperforms both the individual teachers and conventional ensembles at no extra inference cost.