Date post: | 07-Feb-2017 |
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Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, Jeff Clune
[GitHub] [Arxiv]
Slides by Víctor GarciaUPC Computer Vision Reading Group (27/01/2017)
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Introduction
Interpretation of different frameworks to generate images maximizing:
p(x, y) = p(x)*p(y|x)
Prior Condition
Encourages to look realistic
Encourages to look from a particular class
Introduction
Image Generation:
● High Resolution Images (227x227)
GANs struggle to Generate >64x64 Images
Introduction
Image Generation:
● High Resolution Images
● Intra-Class Variance
● Inter-Class Variance (1000-ImageNet classes)
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Probabilistic Interpretation of the methodMetropolis-adjusted Langevin algorithm (MALA) which is a MCMC algorithm for iteratively producing random samples from a distribution p(x):
Probabilistic Interpretation of the methodMetropolis-adjusted Langevin algorithm (MALA) which is a MCMC algorithm for iteratively producing random samples:
Current state
Probabilistic Interpretation of the methodMetropolis-adjusted Langevin algorithm (MALA) which is a MCMC algorithm for iteratively producing random samples:
Future State Current state
Probabilistic Interpretation of the methodMetropolis-adjusted Langevin algorithm (MALA) which is a MCMC algorithm for iteratively producing random samples:
Future State Current state Gradient to the natural manifold of
p(x)
Probabilistic Interpretation of the methodMetropolis-adjusted Langevin algorithm (MALA) which is a MCMC algorithm for iteratively producing random samples:
Gradient to the natural manifold of
p(x)
NoiseFuture State Current state
Probabilistic Interpretation of the method
Future State Current state Gradient to the natural manifold
of p(x)
Noise
Probabilistic Interpretation of the method
p(x)
Step towards an image that causes the classifier to produce a higher score for class C
Step towards a more generic image
Noise
Probabilistic Interpretation of the method
y_co = Content activations y_st = Style activationsRough example
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Method | DGN-AM: sampling without a learned priorDeep Generator Network-based Activation Maximization
It is faster if we move over h subspace instead of the x
fc6AlexNet
Method | DGN-AM: sampling without a learned priorDeep Generator Network-based Activation Maximization
Discriminator 1/0
AlexNet
fc6
Method | DGN-AM: sampling without a learned priorOnce we trained the network G we find the equation for the MALA algorithm
Method | DGN-AM: sampling without a learned priorOnce we trained the network G we find the equation for the MALA algorithm
Method | DGN-AM: sampling without a learned priorOnce we trained the network G we find the equation for the MALA algorithm
Method | DGN-AM: sampling without a learned priorOnce we trained the network G we find the equation for the MALA algorithm
No learned prior No noise
Method | DGN-AM: sampling without a learned prior
+ Different modes from different starts- Same image after many steps- Low mixing speed
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Method | PPGN-h: Generator and DAE model of p(h)
A 7 layers DAE is added to model the prior p(h) in order to increase the mixing speed
Method | PPGN-h: Generator and DAE model of p(h)
The equation is the following:
Prior p(h) Conditioned Gradient
Noise
Method | PPGN-h: Generator and DAE model of p(h)- Similar to the last case. Low diversity- p(h) model learned by DAE is too simple
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Method | Joint PPGN-h: joint Generator and DAEIn order to model p(h) in a more complex way
DAE: h/fc6 → ? → h/fc6
Method | Joint PPGN-h: joint Generator and DAEIn order to model p(h) in a more complex way
DAE: h/fc6 → ? → h/fc6
Joint Generator and DAE: h/fc6 x h/fc6G E
Method | Joint PPGN-h: joint Generator and DAEIn order to model p(h) in a more complex way
DAE: h/fc6 → ? → h/fc6
Joint Generator and DAE: h/fc6 x h/fc6G E
With the same existing network we train the Generator G to act as a DAE in conjunction with the E network
Method | Joint PPGN-h: joint Generator and DAE Noise sweepsFor the last model we test the reconstruction of different h/fc6 vectors when adding different noise levels:
fc6N(0, ) +
Method | Joint PPGN-h: joint Generator and AE Noise sweepsFor the last model we test the reconstruction of different h/fc6 vectors when adding different noise levels:
Method | Joint PPGN-h: joint Generator and AE Noise sweeps
We can still recover large information from the image when mapping with a lot of noise.Many → one.
Method | Joint PPGN-h: joint Generator and DAE Combination of Losses
Comparison of Losses:
● Real Images
●
●
●
●
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Index● Introduction ● Probabilistic Interpretation of the method● Methods and Experiments
○ PPGN-x: DAE model of p(x)○ DGN-AM: sampling without a learned prior○ PPGN-h: Generator and DAE model of p(h)○ Joint PPGN-h: joint Generator and DAE
● Further Experiments○ Image Generation: Captioning○ Image Generation: Multifaceted Feature Visualization○ Image inpainting
● Conclusions
Conclusions
● Only using GANs for the reconstruction, GANs collapse into fewer modes, far from the original p(x).
● Using extra Losses it is possible to better reconstruct the images even for 1000 classes and for higher resolution. Mapping one-to-one helps to prevent typical latent → missing modes.
● It would be great to generate also the embedding space for this super-resolution multi-class images instead of using a supervised learned space.