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Found 4 papers, 2 papers with code
LSHR-Net: a hardware-friendly solution for high-resolution computational imaging using a mixed-weights neural network
However, to date, there have been two major drawbacks: (1) the high-precision real-valued sensing patterns proposed in the majority of existing works can prove problematic when used with computational imaging hardware such as a digital micromirror sampling device and (2) the network structures for image reconstruction involve intensive computation, which is also not suitable for hardware deployment.
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning
Our model features three novel components: First is a feed-forward embedding that takes random class support samples (after a customary CNN embedding) and transfers them to a better class representation in terms of a classification problem.
Dynamic Spectrum Matching with One-shot Learning
The effectiveness of the classification using CNNs drops rapidly when only a small number of spectra per substance are available for training (which is a typical situation in real applications).
Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species.
