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Texture synthesis is the process of algorithmically constructing a large digital image from a small digital sample image by taking advantage of its structural content. It is an object of research in computer graphics and is used in many fields, amongst others digital image editing, 3D computer graphics and post-production of films.
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 ...
Once the network parameters have converged an additional training step is performed using the in-domain data to fine-tune the network weights, this is known as transfer learning. Furthermore, this technique allows convolutional network architectures to successfully be applied to problems with tiny training sets.
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.
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.
Sparse dictionary learning has been successfully applied to various image, video and audio processing tasks as well as to texture synthesis [15] and unsupervised clustering. [16] In evaluations with the Bag-of-Words model, [ 17 ] [ 18 ] sparse coding was found empirically to outperform other coding approaches on the object category recognition ...
WASHINGTON (Reuters) -The FBI has been investigating a longtime Exxon Mobil consultant over the contractor's alleged role in a hack-and-leak operation that targeted hundreds of the oil company’s ...
Using convolutional neural networks was feasible due to the use of graphics processing units (GPUs) during training, [16] an essential ingredient of the deep learning revolution. According to The Economist, "Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole." [4] [17] [18]