1. Image to Image Translation using GANs
Shashank Verma

2. Executive Summary
Objective: Domain Transfer between MNIST and SVHN datasets
Overview:
Generate fake MNIST images with random noise input.
Generate SVHN-domain digits with MNIST and vice versa.
Compare the different GAN architectures trained.
MNIST
(Handwritten Digits)
SVHN
(Street-View House Numbers)

3. Method – Part I
First, we learn GANs (Generative Adversarial Networks) as a method to generate fake MNIST Images
Vanilla GAN (proposed by Ian Goodfellow et al.)
Deep Convolutional GAN (DCGAN)

4. Results (Part I) – Generated Images
Vanilla GAN
No. of Epochs: 200
Batch Size: 100
Optimizer: ADAM
Loss: BCE
Learning Rate =
DCGAN
No. of Epochs: 23
Batch Size: 100
Optimizer: ADAM
Loss: BCE
Learning Rate =

5. Method – Part II MNIST -> SVHN (and vice versa) Domain Transfer
Cycle Consistent Adversarial Nets (CycleGANs) - Discriminators output a probability
Semi Supervised GAN (SGAN) – Discriminators output labels
High Level CycleGAN Architecture

6. Results – Part II (CycleGAN)
MNIST -> SVHN
SVHN -> MNIST
In most cases the correct digit information hasn’t been translated
It is generating images that look like digits, and in the expected style
Training Iterations: 91000
Loss: Reconstruction Loss + Squared Error
Optimizer: ADAM
Learning Rate:
Momentum: 0.5

7. Results – Part II (SGAN)
MNIST -> SVHN
SVHN -> MNIST
Much better results as compared to CycleGAN
Most translated images have correct digit information
Training Iterations:
Loss: Cross Entropy Loss
Optimizer: ADAM
Learning Rate:
Momentum: 0.5

8. Discussion
DCGAN performed better than Vanilla GAN in generating fake MNIST images.
DCGANs have more stable training dynamics as compared to Vanilla GANs.
(Use Convolution layers where you can!)
SGAN performed better than CycleGAN in the task of MNIST-SVHN domain transfer. In SGAN, the correct digit information was retained on more occasions as compared to CycleGAN.
SGANs are a semi-supervised method requiring labeled data!
GANs are hard to train! (especially Vanilla GAN) They suffer with “mode collapse”,
“vanishing gradient” etc.
A method to quantitatively compare domain transfer output is using off-the-shelf classifiers on the generated images.

9. Appendix Framework: PyTorch 0.4.1
Hardware: Euler Cluster (Intel® Haswell CPU (4 Cores in use)
+ Nvidia GeForce GTX-1080 (x 2))
Code Location: