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Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
[article]
2019
arXiv
pre-print
Generic Image recognition is a fundamental and fairly important visual problem in computer vision. One of the major challenges of this task lies in the fact that single image usually has multiple objects inside while the labels are still one-hot, another one is noisy and sometimes missing labels when annotated by humans. In this paper, we focus on tackling these challenges accompanying with two different image recognition problems: multi-model ensemble and noisy data recognition with a unified
arXiv:1908.08520v1
fatcat:amvxtn7wuva3jpjr2fn6brluoq