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Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
[article]
2020
arXiv
pre-print
Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale human-annotated datasets for achieving state-of-the-art results. In turn, the efficacy of supervised learning may be limited by the size of the human annotated dataset. This limitation is particularly notable for image segmentation tasks, where the expense of human annotation is especially large, yet large amounts of unlabeled data may exist. In
arXiv:2005.10266v4
fatcat:dgq3kf7j4fdi3kejbdnbyhv3lm