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Found 5 papers, 0 papers with code
Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification
This paper introduces a novel approach to leverage the knowledge of existing expert models for training new Convolutional Neural Networks, on domains where task-specific data are limited or unavailable.
A comprehensive study of sparse representation techniques for offline signature verification
In this work, a feature extraction method for offline signature verification is presented that harnesses the power of sparse representation in order to deliver state-of-the-art verification performance in several signature datasets like CEDAR, MCYT-75, GPDS and UTSIG.
Attention-Aware Generative Adversarial Networks (ATA-GANs)
Using the attention maps produced by a Teacher- Network we are able to improve the quality of the generated images as well as perform weakly object localization on the generated images.
Offline Handwritten Signature Modeling and Verification Based on Archetypal Analysis
The output of the archetypal analysis of few reference samples is a set of archetypes which are used to form the base of the feature space.
On the definition of Shape Parts: a Dominant Sets Approach
In the present paper a novel graph-based approach to the shape decomposition problem is addressed.
