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Generic Image recognition is a fundamental and fairly important visualproblem in computer vision. One of the major challenges of this task lies inthe fact that single image usually has multiple objects inside while the labelsare still one-hot, another one is noisy and sometimes missing labels whenannotated by humans. In this paper, we focus on tackling these challengesaccompanying with two different image recognition problems: multi-modelensemble and noisy data recognition with a unified framework. As is well-known,usually the best performing deep neural models are ensembles of multiplebase-level networks, as it can mitigate the variation or noise containing inthe dataset. Unfortunately, the space required to store these many networks,and the time required to execute them at runtime, prohibit their use inapplications where test sets are large (e.g., ImageNet). In this paper, wepresent a method for compressing large, complex trained ensembles into a singlenetwork, where the knowledge from a variety of trained deep neural networks(DNNs) is distilled and transferred to a single DNN. In order to distilldiverse knowledge from different trained (teacher) models, we propose to useadversarial-based learning strategy where we define a block-wise training lossto guide and optimize the predefined student network to recover the knowledgein teacher models, and to promote the discriminator network to distinguishteacher vs. student features simultaneously. Extensive experiments onCIFAR-10/100, SVHN, ImageNet and iMaterialist Challenge Dataset demonstrate theeffectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEALachieves top-1/5 21.79%/5.99% val error, which outperforms the original modelby 2.06%/1.14%. On iMaterialist Challenge Dataset, our MEAL obtains aremarkable improvement of top-3 1.15% (official evaluation metric) on a strongbaseline model of ResNet-101.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement