• To put in simple terms, Autoencoders are simply feature extractors with additional task of
  reconstructiong the given input which maybe images or videos with minimum losses.
• In Modern neural networks the autoencoders are utilized for many other purposes like that of
  image segmentation (refer U-nets), image denoising, image completion or also in modern reinforcement learning algorithms (Graph neural networks).

• Convolutional Autoencoders work well untill scaled down to 1x1 feature levels.
• One can not be hopeful of generating a new image using only the trained conv-generator.
• Making the autoencoder conditional does not help to improve the quality of images reproduced.

This is my best attempt at reproducing the images using a conv-autoencoder for code size of 2x2 features.

• Variational Autoencoders are a link between the autoencoders and generative networks. Simply, they are
  also able to sample and create new images better than conv-autoencoders.
• It has a additional KL-Divergence loss which aims to remove inter-dependibility of each feature of the bottleneck part and spread it in a lower bound known to resample it.

• Here I wanted to use only 40 features so Image reconstructions are not very clear but should be better with
  more features on last part of encoder
• I have also made only the decoder part of network conditional by supplying additional labels. (previous experiments
  of making the whole network conditional also gives same results).
• Tried using Resnet Type network for encoder but should work fine with normal encoders also.

I know it is not the best but it created this only using 40 features and also for a complex dataset then MNIST.

• Image completion.
• Denoising images.