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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.
Convolutional neural network – a convolutional neural net where the convolution is performed along the time axis of the data is very similar to a TDNN. Recurrent neural networks – a recurrent neural network also handles temporal data, albeit in a different manner. Instead of a time-varied input, RNNs maintain internal hidden layers to keep ...
At the time of publication, there was no framework available for GPU-based neural network training and inference. The codebase for AlexNet was released under a BSD license, and had been commonly used in neural network research for several subsequent years.
Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction, super-resolution, and inpainting. Image statistics are captured by the structure of a ...
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.