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  2. Image segmentation - Wikipedia

    en.wikipedia.org/wiki/Image_segmentation

    Semantic segmentation is an approach detecting, for every pixel, the belonging class. [18] ... Estimate class statistics based on the random segmentation model defined.

  3. 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]

  4. Semantic network - Wikipedia

    en.wikipedia.org/wiki/Semantic_network

    The semantic link network was systematically studied as a semantic social networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004. [20]

  5. SqueezeNet - Wikipedia

    en.wikipedia.org/wiki/SqueezeNet

    SqueezeNet was originally described in SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. [1] AlexNet is a deep neural network that has 240 MB of parameters, and SqueezeNet has just 5 MB of parameters.

  6. Distributional semantics - Wikipedia

    en.wikipedia.org/wiki/Distributional_semantics

    There is a rich variety of computational models implementing distributional semantics, including latent semantic analysis (LSA), [10] [11] Hyperspace Analogue to Language (HAL), syntax- or dependency-based models, [12] random indexing, semantic folding [13] and various variants of the topic model. [14] Distributional semantic models differ ...

  7. Zero-shot learning - Wikipedia

    en.wikipedia.org/wiki/Zero-shot_learning

    In natural language processing, the key technical direction developed builds on the ability to "understand the labels"—represent the labels in the same semantic space as that of the documents to be classified. This supports the classification of a single example without observing any annotated data, the purest form of zero-shot classification.

  8. Scale-space segmentation - Wikipedia

    en.wikipedia.org/wiki/Scale-space_segmentation

    A one-dimension example of scale-space segmentation. A signal (black), multi-scale-smoothed versions of it (red), and segment averages (blue) based on scale-space segmentation The dendrogram corresponding to the segmentations in the figure above. Each "×" identifies the position of an extremum of the first derivative of one of 15 smoothed ...

  9. Semantic data model - Wikipedia

    en.wikipedia.org/wiki/Semantic_data_model

    The relationship of "Semantic data models" with "physical data stores" and "real world". [1] A semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with ...