my recent presentation on an incredible piece of work in the AI field. this presentation will explain paper named InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets in simple words.

1. InfoGAN: Bridging the
Gap Between Data and
Understanding in GANs
Presenter
Faezeh Maghsoodifar
PhD Student

2. Introduction
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
Think!
How can we give specific meaning to each dimension within a model's latent
space to control specific attributes of the generated data?
2
GANs
InfoGANs Process Of Maximizing
Introduction Experiments Conclusion

3. Introduction
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
3
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
GANs
InfoGANs Process Of Maximizing
Introduction Experiments Conclusion

4. InfoGANs
4
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Traditional GAN
with extra
component Q
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
GANs Process Of Maximizing
InfoGANs
Introduction Experiments Conclusion
Mutual information (MI)
Is between c, G

5. InfoGANs
5
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Traditional GAN
with extra
component Q
DOI: 10.1115/1.4044076
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
GANs Process Of Maximizing
InfoGANs
Introduction Experiments Conclusion

6. Generative Adversarial Networks
6
Generative Adversarial Networks
Generate data
(fake data)
Generator & discriminator,
each competing to win.
Fully Connected
Generator
trying to fake
Discriminator,
trying not to be fooled
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
6
6
6
GANs Process Of Maximizing
InfoGANs
Introduction Experiments Conclusion
Deep Convolutional

7. Generative Adversarial Networks
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Problem with Traditional GANs
1. Changing one dimension in a multi-dimensional does not have a clear associated meaning.
2. Latent vectors lack interpretable semantics, leading to unpredictable changes in outputs.
Formulation of GANs
7
7
GANs Process Of Maximizing
InfoGANs
Introduction Experiments Conclusion

8. InfoGANs
8
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Mutual information (MI)
Is between
DOI: 10.1115/1.4044076
1 Latent Code → c
2 Generated Images → G(z,c)
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
GANs Process Of Maximizing
InfoGANs
Introduction Experiments Conclusion

9. Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Mutual information measures the
reduction of uncertainty in one
variable when another is observed, a
concept central to InfoGAN's approach.
The lower bound of mutual
information can be approximated and
maximized using Monte Carlo
simulation, streamlining the training
process.
01
02
03
Information
Theory
Maximization
Technique
Monte Carlo
Simulation
P
r
o
c
e
s
s
O
f
M
a
x
i
m
i
zing
Process Of Maximizing MI
InfoGAN employs variational
information maximization, a
technique that provides a lower
bound to the mutual information,
facilitating its maximization.
By using Lemma:
Q directly and
G via the re-parametrization trick
Can be maximized
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
9
GANs
InfoGANs
Introduction Process Of Maximizing
9
Experiments Conclusion

10. InfoGANs
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Finally, the resulting algorithm calls
Information Maximizing Generative Adversarial Networks (InfoGAN)
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
10
GANs
InfoGANs
Introduction Process Of Maximizing
1
0
Experiments Conclusion

11. Datasets
MNIST Dataset
CelebA Dataset
SVHN Dataset
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
3D Faces Dataset
3D Chairs Dataset
GANs
InfoGANs Process Of Maximizing
Introduction
11
Experiments Conclusion

12. What They
Got
MNIST Dataset
Purpose: To disentangle digit shape from style.
The model was used to change the types of digits and styles, such as rotation and
width, and to show the model's ability to generalize well beyond its training range.
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
Disentangled Representation
Evaluation
InfoGAN successfully disentangles digit shape from style, with latent codes capturing
rotation and width, demonstrating natural-looking variations.
GANs
InfoGANs Process Of Maximizing
Introduction
12
Experiments Conclusion

13. What They
Got
3D Faces Dataset
On the faces’ dataset, InfoGAN learns to represent azimuth, elevation, and lighting as
continuous latent variables without supervision.
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
Disentangled Representation
Evaluation
Purpose: To learn interpretable representations of facial features without supervision.
InfoGAN was used to manipulate features such as pose (azimuth), elevation, and
lighting, demonstrating its ability to discover variations autonomously.
GANs
InfoGANs Process Of Maximizing
Introduction
13
Experiments Conclusion

14. What They
Got
3D Chairs Dataset
InfoGAN demonstrates its ability to interpolate between chair types, capturing
rotation and width variations with continuous latent codes.
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
Disentangled Representation
Evaluation
Purpose: To learn representations of object features such as rotation and width.
The model learned to interpolate between similar types of chairs and adjust their
widths using a single continuous code, showing its capability in understanding and
varying object dimensions.
GANs
InfoGANs Process Of Maximizing
Introduction
14
Experiments Conclusion

15. What They
Got
Street View House Numbers (SVHN) dataset
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
Disentangled Representation
Evaluation
Purpose: To learn interpretable representations from a noisy and less uniform dataset.
InfoGAN was tested on its ability to handle real-world complexity and variability in
image resolution and background distractions.
GANs
InfoGANs Process Of Maximizing
Introduction
15
Experiments Conclusion

16. What They
Got
CelebA dataset (celebrity faces)
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
Disentangled Representation
Evaluation
Purpose: To learn and disentangle complex visual concepts from a dataset with large
variations.
The model was used to control and understand diverse attributes in celebrity
images, such as pose, presence of eyeglasses, hairstyles, and emotions, even
without having multiple images of the same person in different poses
GANs
InfoGANs Process Of Maximizing
Introduction
16
Experiments Conclusion

17. Conclusion
Faezeh Maghsoodifar InfoGAN: Bridging the Gap Between Data and Understanding in GANs Feb 2024
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." https://arxiv.org/abs/1606.03657 (2016).
1. InfoGAN assigns clear meanings to each dimension of the
latent space.
2. Each dimension of the hidden variable represents a distinct
semantic feature.
3. Enables control over specific attributes like handwriting style,
digit shape, and background in images.
4. InfoGAN aims to maximize mutual information between c and
G to retain the meaning of c in the generated images.
5. Introduces a lower bound on mutual information that can be
maximized during training.
GANs
InfoGANs Experiments
Process Of Maximizing
Introduction
17
Conclusion

18. Thank you
Faezeh Maghsoodifar
fmaghsoodifar@crimson.ua.edu