enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Latent diffusion model - Wikipedia

    en.wikipedia.org/wiki/Latent_Diffusion_Model

    For example, in text-to-image generation, the text is divided into a sequence of tokens, then encoded by a text encoder, such as a CLIP encoder, before feeding into the backbone. As another example, an input image can be processed by a Vision Transformer into a sequence of vectors, which can then be used to condition the backbone for tasks such ...

  3. Kernel (image processing) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(image_processing)

    For example, attempting to read a pixel 3 units outside an edge reads one 3 units inside the edge instead. Crop / Avoid overlap Any pixel in the output image which would require values from beyond the edge is skipped. This method can result in the output image being slightly smaller, with the edges having been cropped.

  4. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    CNN layers arranged in 3 dimensions. For example, in CIFAR-10, images are only of size 32×32×3 (32 wide, 32 high, 3 color channels), so a single fully connected neuron in the first hidden layer of a regular neural network would have 32*32*3 = 3,072 weights. A 200×200 image, however, would lead to neurons that have 200*200*3 = 120,000 weights.

  5. Image derivative - Wikipedia

    en.wikipedia.org/wiki/Image_derivative

    Image derivatives can be computed by using small convolution filters of size 2 × 2 or 3 × 3, such as the Laplacian, Sobel, Roberts and Prewitt operators. [1] However, a larger mask will generally give a better approximation of the derivative and examples of such filters are Gaussian derivatives [ 2 ] and Gabor filters . [ 3 ]

  6. LeNet - Wikipedia

    en.wikipedia.org/wiki/LeNet

    LeNet-4 was a larger version of LeNet-1 designed to fit the larger MNIST database. It had more feature maps in its convolutional layers, and had an additional layer of hidden units, fully connected to both the last convolutional layer and to the output units. It has 2 convolutions, 2 average poolings, and 2 fully connected layers.

  7. Stable Diffusion - Wikipedia

    en.wikipedia.org/wiki/Stable_Diffusion

    Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...

  8. Just how fast is Miami sinking into the Atlantic Ocean? - AOL

    www.aol.com/just-fast-miami-sinking-atlantic...

    MIAMI – A new study from the University of Miami shows dozens of luxury, beachfront condos and hotels, all along the southeast coast of Florida, are sinking into the ground at unexpected rates ...

  9. Inception (deep learning architecture) - Wikipedia

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

    It also uses a form of dimension-reduction by concatenating the output from a convolutional layer and a pooling layer. As an example, a tensor of size 35 × 35 × 320 {\displaystyle 35\times 35\times 320} can be downscaled by a convolution with stride 2 to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} , and by maxpooling with pool size ...