3 accepted papers
Implicit Neural Representations (INRs) are continuous function learners for conventional digital signal representations. With the aid of positional embeddings and/or exhaustively fine-tuned activation functions, INRs have surpassed many limitations of traditional discrete representations. However, e…
Implicit Neural Representations (INRs) provide a continuous mapping between the coordinates of a signal and the corresponding values. As the performance of INRs heavily depends on the choice of nonlinear-activation functions, there has been a significant focus on encoding explicit signals within INR…
Sparse representation based classification has gained popularity with geospatial image analysis in general and hyperspectral image analysis in particular. A central idea with such classification approaches is that a test pixel (spectral reflectance vector) can be sparsely represented in a training d…