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Patch-based texture synthesis creates a new texture by copying and stitching together textures at various offsets, similar to the use of the clone tool to manually synthesize a texture. Image quilting [8] and graphcut textures [9] are the best known patch-based texture synthesis algorithms. These algorithms tend to be more effective and faster ...
The original paper used a VGG-19 architecture [5] that has been pre-trained to perform object recognition using the ImageNet dataset. In 2017, Google AI introduced a method [6] that allows a single deep convolutional style transfer network to learn multiple styles at the same time. This algorithm permits style interpolation in real-time, even ...
The time delay neural network (TDNN) was introduced in 1987 by Alex Waibel et al. for phoneme recognition and was one of the first convolutional networks, as it achieved shift-invariance. [43] A TDNN is a 1-D convolutional neural net where the convolution is performed along the time axis of the data.
DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like appearance reminiscent of a psychedelic experience in the deliberately overprocessed images.
Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and sea urchins, which are correlated with "nodes" that represent visual features. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and ...
LeNet-5 architecture (overview). LeNet is a series of convolutional neural network structure proposed by LeCun et al.. [1] The earliest version, LeNet-1, was trained in 1989.In general, when "LeNet" is referred to without a number, it refers to LeNet-5 (1998), the most well-known version.
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
Texture synthesis from an example texture. Super-resolution, inferring a high-resolution image from a low-resolution source. ... are created using a simple "painting ...