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Found 8 papers, 0 papers with code
DoDo-Code: a Deep Levenshtein Distance Embedding-based Code for IDS Channel and DNA Storage
In this paper, we propose an innovative approach that utilizes deep Levenshtein distance embedding to bypass these mathematical challenges.
Levenshtein Distance Embedding with Poisson Regression for DNA Storage
Under this embedding dimension, the Poisson regression is introduced by assuming the Levenshtein distance between sequences of fixed length following a Poisson distribution, which naturally aligns with the definition of Levenshtein distance.
Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage
The Levenshtein distance is approximated by the squared Euclidean distance between the embedding vectors, which is fast calculated and clustering algorithm friendly.
Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm
In recent years, Graph Neural Network (GNN) has bloomly progressed for its power in processing graph-based data.
Improving Deep Hyperspectral Image Classification Performance with Spectral Unmixing
Recent advances in neural networks have made great progress in the hyperspectral image (HSI) classification.
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field
To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural networks.
Ranked #8 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric)
General Classification
Hyperspectral Image Classification
+3
A CNN-based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification
At the testing stage, by applying the discriminant model to the pixel-pairs generated by the test pixel and its neighbors, the local structure is estimated and represented as a customized convolutional kernel.
Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery
In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery.
