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Unsupervised Feature Learning Via Non
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Self Organizing Maps: Fundamentals
www.cs.bham.ac.ukWe now turn to unsupervised training, in which the networks learn to form their own ... The effect of each learning weight update is to move the weight vectors wi of the winning ... Our SOM provides such a non-linear transformation called a feature map. The stages of the SOM algorithm can be summarised as follows: 1.
Rich Feature Hierarchies for Accurate Object Detection and ...
openaccess.thecvf.comthis problem is to use unsupervised pre-training, followed by supervised fine-tuning (e.g., [29]). The second major contribution of this paper is to show that supervised pre-training on a large auxiliary dataset (ILSVRC), followed by domain-specific fine-tuning on a small dataset (PASCAL), is an effective paradigm for learning high-capacity CNNs