Search results
Results From The WOW.Com Content 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.
Blinn–Phong reflection model; Bloom (shader effect) Bounding volume; Bounding volume hierarchy; Bresenham's line algorithm; Bump mapping; Collision detection; Color space; Colour banding; Compositing; Computational geometry; Computer animation; Computer graphics; Computer graphics (computer science) Computer-generated imagery; Constructive ...
This is a list of models and meshes commonly used in 3D computer graphics for testing and demonstrating rendering algorithms and visual effects. Their use is important for comparing results, similar to the way standard test images are used in image processing.
A drawing of a graph or network diagram is a pictorial representation of the vertices and edges of a graph. This drawing should not be confused with the graph itself: very different layouts can correspond to the same graph. [2] In the abstract, all that matters is which pairs of vertices are connected by edges.
3D computer graphics rely on many of the same algorithms as 2D computer vector graphics in the wire-frame model and 2D computer raster graphics in the final rendered display. In computer graphics software, 2D applications may use 3D techniques to achieve effects such as lighting, and similarly, 3D may use some 2D rendering techniques.
The graph convolutional network (GCN) was first introduced by Thomas Kipf and Max Welling in 2017. [9] A GCN layer defines a first-order approximation of a localized spectral filter on graphs. GCNs can be understood as a generalization of convolutional neural networks to graph-structured data. The formal expression of a GCN layer reads as follows:
More simply, an augmenting path is an available flow path from the source to the sink. A network is at maximum flow if and only if there is no augmenting path in the residual network G f. The bottleneck is the minimum residual capacity of all the edges in a given augmenting path. [2] See example explained in the "Example" section of this article.
Included are diagram techniques, chart techniques, plot techniques, and other forms of visualization. There is also a list of computer graphics and descriptive geometry topics . Simple displays