This document proposes using generative adversarial networks (GANs) to generate additional training data samples for a convolutional neural network (CNN) classifier in order to address problems with limited training data and conditions not covered in the original dataset. Specifically, the author proposes training a GAN model to generate new image samples under different conditions and combining this synthetic data with the original dataset to train the CNN. The accuracy of the CNN will then be compared when trained on just the original data versus when trained on the combined original and synthetic datasets. This approach aims to improve the CNN's performance by expanding the variety of training conditions it sees through data augmentation with GANs.

1. Adaptive Learning Based on Deep
Generative Adversarial Networks
Proposed Thesis Project
Muhanad Shab Kaleia

2. Adaptive Learning Based on GANs 2
Generative Adversarial Networks

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Generative Adversarial Networks
● Working at OpenAI.
● Invented GANs in 2014
● Used heavily at
Facebook.

4. Adaptive Learning Based on GANs 4
Source: https://goo.gl/jPB6wM

5. Adaptive Learning Based on GANs 5Source: https://goo.gl/jPB6wM

6. Adaptive Learning Based on GANs 6
Source: https://goo.gl/jPB6wM

7. Adaptive Learning Based on GANs 7

8. Adaptive Learning Based on GANs 8
Applications

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Source: https://youtu.be/HN9NRhm9waY?t=18m8s

10. Adaptive Learning Based on GANs 10Source: https://goo.gl/jPB6wM

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Next frame generation

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Adaptive Training Based on GAN
● Problems:
– The need of huge training set
– The need to cover most of condition (colors,
lighting, angles)
● My idea:
– Use GANs model to generate new set of
samples, in different conditions
– Train a CNN with both generated and the
original datasets
– Compare the accuracy results before and
after adding the GANs in the system

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References
[1] Goodfellow, I. J., Pouget-abadie, J., Mirza, M., Xu, B., & Warde-farley,
D. (n.d.). Generative Adversarial Nets, 1–9.
[2] Mathieu, M., Couprie, C., & LeCun, Y. (2015). Deep multi-scale video
prediction beyond mean square error. Iclr, (2015), 1–14. Retrieved from
http://arxiv.org/abs/1511.05440
[3] https://goo.gl/W4CEYY
[4] https://goo.gl/DVs01u
[5] https://goo.gl/lNuUDD