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 # Fooling Code
-
 This is the code base used to reproduce the "fooling" images in the paper:
-
 [Nguyen A](http://anhnguyen.me), [Yosinski J](http://yosinski.com/), [Clune J](http://jeffclune.com). ["Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"](http://arxiv.org/abs/1412.1897). In Computer Vision and Pattern Recognition (CVPR '15), IEEE, 2015.
-
-**If you use this software in an academic article, please cite:**
-
-    @inproceedings{nguyen2015deep,
-      title={Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images},
-      author={Nguyen, Anh and Yosinski, Jason and Clune, Jeff},
-      booktitle={Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on},
-      year={2015},
-      organization={IEEE}
-    }
-
-For more information regarding the paper, please visit www.evolvingai.org/fooling
-
-## Requirements
-This is an installation process that requires two main software packages (included in this package):
-
-1. Caffe: http://caffe.berkeleyvision.org
-  * Our libraries installed to work with Caffe
-    * Cuda 6.0
-    * Boost 1.52
-    * g++ 4.6
-  * Use the provided scripts to download the correct version of Caffe for your experiments.
-    * `./download_caffe_evolutionary_algorithm.sh` Caffe version for EA experiments
-    * `./download_caffe_gradient_ascent.sh` Caffe version for gradient ascent experiments
-2. Sferes: https://github.com/jbmouret/sferes2
-  * Our libraries installed to work with Sferes
-    * OpenCV 2.4.10
-    * Boost 1.52
-    * g++ 4.9 (a C++ compiler compatible with C++11 standard)
-
-Note: These are specific versions of the two frameworks with our additional work necessary to produce the images as in the paper. They are not the same as their master branches.
-
-## Installation
-
-Please see the [Installation_Guide](https://github.com/Evolving-AI-Lab/fooling/wiki/Installation-Guide) for more details.
-
-## Usage
-
-* An MNIST experiment (Fig. 4, 5 in the paper) can be run directly on a local machine (4-core) within a reasonable amount of time (around ~5 minutes or less for 200 generations).
-* An ImageNet experiment needs to be run on a cluster environment. It took us ~4 days x 128 cores to run 5000 generations and produce 1000 images (Fig. 8 in the paper). 
-* [How to configure an experiment to test the evolutionary framework quickly](https://github.com/Evolving-AI-Lab/fooling/wiki/How-to-test-the-evolutionary-framework-quickly)
-* To reproduce the gradient ascent fooling images (Figures 13, S3, S4, S5, S6, and S7 from the paper), see the [documentation in the caffe/ascent directory](https://github.com/anguyen8/opencv_contrib/tree/master/modules/dnns_easily_fooled/caffe/ascent). You'll need to download the correct Caffe version for this experiment using `./download_caffe_gradient_ascent.sh` script.
-
-## Updates
-
-* A fork project [here](https://github.com/Evolving-AI-Lab/innovation-engine) has support for the **latest Caffe** and experiments to create *recognizable* images instead of unrecognizable.