Large-Margin Representation Learning for Texture Classification. (arXiv:2206.08537v1 [cs.CV])

This paper presents a novel approach combining convolutional layers (CLs) and
large-margin metric learning for training supervised models on small datasets
for texture classification. The core of such an approach is a loss function
that computes the distances between instances of interest and support vectors.
The objective is to update the weights of CLs iteratively to learn a
representation with a large margin between classes. Each iteration results in a
large-margin discriminant model represented by support vectors based on such …

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