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  2. SqueezeNet - Wikipedia

    en.wikipedia.org/wiki/SqueezeNet

    SqueezeNet is a deep neural network for image classification released in 2016. SqueezeNet was developed by researchers at DeepScale , University of California, Berkeley , and Stanford University . In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while achieving competitive accuracy.

  3. LOBPCG - Wikipedia

    en.wikipedia.org/wiki/LOBPCG

    Image segmentation via spectral graph partitioning by LOBPCG with multigrid preconditioning has been first proposed in [53] and actually tested in [54] and. [55] The latter approach has been later implemented in Python scikit-learn [56] that uses LOBPCG from SciPy with algebraic multigrid preconditioning for solving the eigenvalue problem for ...

  4. Caffe (software) - Wikipedia

    en.wikipedia.org/wiki/Caffe_(software)

    Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8] Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. [9] [10]

  5. Medical open network for AI - Wikipedia

    en.wikipedia.org/wiki/Medical_open_network_for_AI

    Medical open network for AI (MONAI) is an open-source, community-supported framework for Deep learning (DL) in healthcare imaging. MONAI provides a collection of domain-optimized implementations of various DL algorithms and utilities specifically designed for medical imaging tasks.

  6. U-Net - Wikipedia

    en.wikipedia.org/wiki/U-Net

    U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]

  7. Vision transformer - Wikipedia

    en.wikipedia.org/wiki/Vision_transformer

    Another encodes the quantized vectors back to image patches. The training objective attempts to make the reconstruction image (the output image) faithful to the input image. The discriminator (usually a convolutional network, but other networks are allowed) attempts to decide if an image is an original real image, or a reconstructed image by ...

  8. Graph cuts in computer vision - Wikipedia

    en.wikipedia.org/wiki/Graph_cuts_in_computer_vision

    As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.

  9. Gradient vector flow - Wikipedia

    en.wikipedia.org/wiki/Gradient_Vector_Flow

    It is usually used to create a vector field from images that points to object edges from a distance. It is widely used in image analysis and computer vision applications for object tracking, shape recognition, segmentation, and edge detection. In particular, it is commonly used in conjunction with active contour model.