Search results
Results from the WOW.Com Content Network
Okular was started for the Google Summer of Code of 2005 by Piotr Szymański. [1] [2] Okular was identified as a success story of the 2007 Season of Usability. [5]In this season, the Okular toolbar mockup was created based on an analysis of other popular document viewers and a usage survey.
As with Adobe Acrobat, Nitro PDF Pro's reader is free; but unlike Adobe's free reader, Nitro's free reader allows PDF creation (via a virtual printer driver, or by specifying a filename in the reader's interface, or by drag-'n-drop of a file to Nitro PDF Reader's Windows desktop icon); Ghostscript not needed. PagePlus: Proprietary: No
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.
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.
In text-to-image retrieval, users input descriptive text, and CLIP retrieves images with matching embeddings. In image-to-text retrieval, images are used to find related text content. CLIP’s ability to connect visual and textual data has found applications in multimedia search, content discovery, and recommendation systems. [31] [32]
In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more.This is accomplished by doing a convolution between the kernel and an image.
AlexNet contains eight layers: the first five are convolutional layers, some of them followed by max-pooling layers, and the last three are fully connected layers. The network, except the last layer, is split into two copies, each run on one GPU. [1] The entire structure can be written as
A two-layer neural network capable of calculating XOR. The numbers within the neurons represent each neuron's explicit threshold. The numbers that annotate arrows represent the weight of the inputs. Note that If the threshold of 2 is met then a value of 1 is used for the weight multiplication to the next layer.