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We reproduce the work in Zero-shot Knowledge Transfer via Adversarial Belief Matching, which describes a novel approach for knowledge transfer. A teacher network trained on real samples distills knowledge to a student network that is trained solely on pseudo data extracted from a generator network, with the student trying to mimic the teacher's outputs on these datapoints. To this end, we additionally re-implement Wide Residual Networks which are used as the main framework for both teacher anddoi:10.5281/zenodo.3818623 fatcat:5kpl7orh5zc2jkwfyq25haofle