Deep-Dream-Pytorch

• Clean and lucid implementation of Deep Drream algorthm in pytorch

Introduction

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 over-processed images.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.

Usage

gh repo clone unboxdisease/Deep-Dream-pytorch
cd Deep-Dream-pytorch
jupyter notebook

Screenshots

Content

• we simply feed the classification network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to iteratively enhance whatever it detected. Each layer of the network deals with features at a different level of abstraction, so the complexity of features we generate depends on which layer we choose to enhance.[1]
• The image is then modified to increase these activations, enhancing the patterns seen by the network, and resulting in a dream-like image.
• The cited resemblance of the imagery to LSD- and psilocybin- induced hallucinations is suggestive of a functional resemblance between artificial neural networks and particular layers of the visual cortex. [2]

Requirements

Pytorch
Opencv-python
Jupyter Notebook

Reference

[1] MORDVINTSEV, ALEXANDER; OLAH, CHRISTOPHER; TYKA, MIKE (2015). "DEEPDREAM - A CODE EXAMPLE FOR VISUALIZING NEURAL NETWORKS". GOOGLE RESEARCH. ARCHIVED FROM THE ORIGINAL ON 2015-07-08. [2] LAFRANCE, ADRIENNE (2015-09-03). "WHEN ROBOTS HALLUCINATE". THE ATLANTIC. RETRIEVED 24 SEPTEMBER 2015.