Search Results for author:
Found 5 papers, 0 papers with code
Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention
Deep learning methods have traditionally been difficult to apply to compression of hyperspectral images onboard of spacecrafts, due to the large computational complexity needed to achieve adequate representational power, as well as the lack of suitable datasets for training and testing.
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification
Deep learning has shown outstanding performance in several applications including image classification.
BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions
We present BioMetricNet: a novel framework for deep unconstrained face verification which learns a regularized metric to compare facial features.
Learning mappings onto regularized latent spaces for biometric authentication
Differently from other methods, RegNet learns a mapping of the input biometric traits onto a target distribution in a well-behaved space in which users can be separated by means of simple and tunable boundaries.
Analysis of SparseHash: an efficient embedding of set-similarity via sparse projections
Embeddings provide compact representations of signals in order to perform efficient inference in a wide variety of tasks.
