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  2. Attention (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Attention_(machine_learning)

    An image captioning model was proposed in 2015, citing inspiration from the seq2seq model. [ 25 ] that would encode an input image into a fixed-length vector. Xu et al (2015), [ 26 ] citing Bahdanau et al (2014), [ 27 ] applied the attention mechanism as used in the seq2seq model to image captioning.

  3. Transformer (deep learning architecture) - Wikipedia

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

    In addition, the scope of attention, or the range of token relationships captured by each attention head, can expand as tokens pass through successive layers. This allows the model to capture more complex and long-range dependencies in deeper layers. Many transformer attention heads encode relevance relations that are meaningful to humans.

  4. Vision transformer - Wikipedia

    en.wikipedia.org/wiki/Vision_transformer

    The attention mechanism in a ViT repeatedly transforms representation vectors of image patches, incorporating more and more semantic relations between image patches in an image. This is analogous to how in natural language processing, as representation vectors flow through a transformer, they incorporate more and more semantic relations between ...

  5. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    Image and video generators like DALL-E (2021), Stable Diffusion 3 (2024), [44] and Sora (2024), use Transformers to analyse input data (like text prompts) by breaking it down into "tokens" and then calculating the relevance between each token using self-attention, which helps the model understand the context and relationships within the data.

  6. Multilayer perceptron - Wikipedia

    en.wikipedia.org/wiki/Multilayer_perceptron

    In 1943, Warren McCulloch and Walter Pitts proposed the binary artificial neuron as a logical model of biological neural networks. [11]In 1958, Frank Rosenblatt proposed the multilayered perceptron model, consisting of an input layer, a hidden layer with randomized weights that did not learn, and an output layer with learnable connections.

  7. Active shutter 3D system - Wikipedia

    en.wikipedia.org/wiki/Active_shutter_3D_system

    An active shutter 3D system (a.k.a. alternate frame sequencing, alternate image, AI, alternating field, field sequential or eclipse method) is a technique for displaying stereoscopic 3D images. It works by only presenting the image intended for the left eye while blocking the right eye's view, then presenting the right-eye image while blocking ...

  8. Hidden-surface determination - Wikipedia

    en.wikipedia.org/wiki/Hidden-surface_determination

    Attempts to model the path of light rays to a viewpoint by tracing rays from the viewpoint into the scene. Although not a hidden-surface removal algorithm as such, it implicitly solves the hidden-surface removal problem by finding the nearest surface along each view-ray. Effectively this is equivalent to sorting all the geometry on a per-pixel ...

  9. Feedforward neural network - Wikipedia

    en.wikipedia.org/wiki/Feedforward_neural_network

    In 1943, Warren McCulloch and Walter Pitts proposed the binary artificial neuron as a logical model of biological neural networks. [16] In 1958, Frank Rosenblatt proposed the multilayered perceptron model, consisting of an input layer, a hidden layer with randomized weights that did not learn, and an output layer with learnable connections. [17 ...