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Found 5 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).
Cystoid macular edema segmentation of Optical Coherence Tomography images using fully convolutional neural networks and fully connected CRFs
As a first step, the framework trains the FCN model to extract features from retinal layers in OCT images, which exhibit CME, and then segments CME regions using the trained model.
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.
