How to generate image using deep learning There are lots of examples of classifier using deep learning techniques with CIFAR-10 datasets. The way it works is that, train thousands of images of cat, dog, plane etc and then classify an image as dog, plane or cat. But I want to do the reverse thing. I want to train dog, cat, planes and it should output images. Here is my idea


Group similar/clear images of cat as value 1, a less similar/blur images as 1.1 and so on. 
Similarly, group similar images of dog as value 2 and less similar images as 2.1 and so on ...
Do the same for all types of images.


Generated dataset should look like this

input    output
 1      pixels(24*24) of a cat images(clear)
 1      pixels(24*24) of a cat images(clear)
 .
 .
 1.1      pixels(24*24) of another cat images(blur) 
 1.1      pixels(24*24) of another cat images(blur) 
  .
  .
  and so on



Now train values of input and label of images as output. Input will be just 1 dimensional, may be I will think of some other data. Output layer will have 24*24 i.e 576 units or neurons.
At the end of training, I want something like this, if I give a input for example 1.15 it should output a new image since we trained with values 1.1 and 1.2 but we didn't train with 1.11 or 1.115.


Please give me some idea how can I do this? Any link to example or papers would be nice.
DCGAN(Deep convolutioanl GAN) or WGAN can be used to generate images by training them on already existing images. GAN are very tricky to train. DCGAN and WGAN are the techniques that make it somewhat easier. 

A GAN consists of a Generator and a Discriminator. Generator's job is to generate images and Discriminator's job is to predict whether the image generated by the generator is fake or real. If the generator succeeds in fooling the discriminator, we can say that generator has succeeded.

I have generated MNIST images using DCGAN, you can easily port the code to generate dogs and cats images.