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  2. Latent diffusion model - Wikipedia

    en.wikipedia.org/wiki/Latent_Diffusion_Model

    The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [3]Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images.

  3. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    If one freezes the rest of the model and only finetune the last layer, one can obtain another vision model at cost much less than training one from scratch. AlexNet block diagram AlexNet is a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton , who was Krizhevsky ...

  4. Grapher - Wikipedia

    en.wikipedia.org/wiki/Grapher

    Grapher is a graphing calculator capable of creating both 2D graphs including classic (linear-linear), polar coordinates, linear-logarithmic, log-log, and polar log, as well as 3D graphs including standard system, cylindrical system, and spherical system. Grapher is a Cocoa application which takes advantage of Mac OS X APIs.

  5. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.

  6. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's receptive field. Typically the area is a square (e.g. 5 by 5 neurons). Whereas, in a fully connected layer, the receptive field is the entire previous layer. Thus, in each convolutional layer, each neuron takes input from a ...

  7. Contrastive Language-Image Pre-training - Wikipedia

    en.wikipedia.org/wiki/Contrastive_Language-Image...

    Take the activations of the highest layer of the transformer on the [EOS], apply LayerNorm, then a final linear map. This is the text encoding of the input sequence. The final linear map has output dimension equal to the embedding dimension of whatever image encoder it is paired with.

  8. Inception (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Inception_(deep_learning...

    Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern

  9. IEEE 802.3 - Wikipedia

    en.wikipedia.org/wiki/IEEE_802.3

    IEEE 802.3 is a working group and a collection of standards defining the physical layer and data link layer's media access control (MAC) of wired Ethernet.The standards are produced by the working group of Institute of Electrical and Electronics Engineers (IEEE).

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