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DeepDream information


The Mona Lisa with DeepDream effect using VGG16 network trained on ImageNet

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.[1][2][3]

Google's program popularized the term (deep) "dreaming" to refer to the generation of images that produce desired activations in a trained deep network, and the term now refers to a collection of related approaches.

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DeepDream

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DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns...

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Pareidolia

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previously. Striking visuals can be produced in this way, notably in the DeepDream software, which falsely detects and then exaggerates features such as...

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Artificial intelligence art

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analyzing a dataset of example images. In 2015, a team at Google released DeepDream, a program that uses a convolutional neural network to find and enhance...

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Deeper into Dream

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Deeper into Dream is the eighth studio album by Australian indie pop musician Ben Lee, released on 11 October 2011 through Lojinx in Europe and Dangerbird...

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TensorFlow

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for the automated image-captioning software DeepDream. Free and open-source software portal Comparison of deep learning software Differentiable programming...

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Psychedelic art

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at the time of creation. Conversely, the convolutional neural network DeepDream finds and enhances patterns in images purely via algorithmic pareidolia...

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OpenAI

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Wired, Brockman met with Yoshua Bengio, one of the "founding fathers" of deep learning, and drew up a list of the "best researchers in the field". Brockman...

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WaveNet

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WaveNet is a deep neural network for generating raw audio. It was created by researchers at London-based AI firm DeepMind. The technique, outlined in a...

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Convolutional neural network

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CNN as their basic framework. The winner GoogLeNet (the foundation of DeepDream) increased the mean average precision of object detection to 0.439329...

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Multimodal learning

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Multimodal learning, in the context of machine learning, is a type of deep learning using a combination of various modalities of data, such as text, audio...

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Deep reinforcement learning

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Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem...

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Types of artificial neural networks

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representations. Examples of applications in computer vision include DeepDream and robot navigation. They have wide applications in image and video recognition...

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Deeper and Deeper

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"Deeper and Deeper" is a song by American singer Madonna from her fifth studio album, Erotica (1992). It was written and produced by both Madonna and...

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Machine learning

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lossless data compression, as demonstrated by DeepMind's research with the Chinchilla 70B model. Developed by DeepMind, Chinchilla 70B effectively compressed...

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Deep belief network

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In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple...

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Dream from the Deep Well

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Dream from the Deep Well is the fourth studio album by Irish folk musician Brigid Mae Power. Editors at AllMusic rated this album 3.5 out of 5 stars,...

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Doing It for the Money

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Daniel Henry and produced by Molly Ortiz. The video utilizes Google's DeepDream Technology that "manipulates reality to create a sensory overload." On...

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Learning rate

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momentum is more complex than for decay but is most often built in with deep learning libraries such as Keras. Time-based learning schedules alter the...

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Dream Corp LLC

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Dream Corp LLC is an American live-action/animated sitcom created by Daniel Stessen for Adult Swim. The series is an absurdist workplace comedy set in...

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Variational autoencoder

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E_{\phi }} , and the decoder as D θ {\displaystyle D_{\theta }} . As in every deep learning problem, it is necessary to define a differentiable loss function...

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