Abstract:Traditional self-supervised learning requires convolutional neural networks (CNNs) using external pretext tasks (i.e., image-or video-based tasks) to encode high-level semantic visual representations. In this paper, we show that feature transformations within CNNs can also be regarded as supervisory signals to construct the self-supervised task, called internal pretext task. And such a task can be applied for the enhancement of supervised learning. Specifically, we first transform the internal feature maps by … Show more
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